WHO CALLS: ACCURATELY GEOCODING AND CREATING A HOT-SPOT ANALYSIS OF CALLS FOR SERVICE (CFS) IN LAKE COUNTY

(10/1/2012 – 10/1/2015)

BY

BRANDON BARNETT

A TERMINAL PROECT PRESENTED TO THE GRADUATE SCHOOLOF THE UNIVERSITY OF FLORIDA IN PARTIAL FULFILLMENT

OF THE REQUIREMENTS FOR THE DEGREE OFMASTER OF URBAN AND REGIONAL PLANNING

UNIVERSITY OF FLORIDA

2015

© 2015 Brandon Barnett

DEDICATIONS

To my loving and supporting family: Mom, Britta, Nana, and Papa, without your love and support I would have never made it this far.

To Sarah, my future wife, thank you for keeping me motivated and focused throughout this degree program.

To Mr. Aidan Holmes and Jim Root who helped me extract and interpret the data, this project wouldn’t have been possible without you.

ACKNOWLEDGMENTS

To Lake Emergency Medical Services including: Cynde Earls, Aidan Holmes, and Jim Root for helping me aggregate and interpret the data.

To Lake County Fire Rescue including: Brent Bentley, Randy Jones, Chief John Joliff, and Laura Kirkland for their support and interest in the project.

To Lake County I.T. especially: Sue Carroll, Steve Earls, Leon Platt, and Erikk Ross for the support and freedom to pursue this project. Also a special thanks to Jim Dowling

and Richard Helfst for helping me with some of the cartographic elements and schemas.

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TABLE OF CONTENTS

Page

ACKNOWLEDGMENTS...................................................................................................4

LIST OF ABBREVIATIONS..............................................................................................7

ABSTRACT......................................................................................................................8

INTRODUCTION..............................................................................................................9

GIS Analytical Process..............................................................................................9Lake County Demographics.....................................................................................11

PROJECT RATIONALE.................................................................................................14

CFS Analysis is Complicated...................................................................................14Return on Investment...............................................................................................15

LITERATURE REVIEW..................................................................................................17

Geocoding...............................................................................................................17Hot-Spot Analysis....................................................................................................18GIS & Other Governmental Agencies......................................................................21GIS & Cost of Living.................................................................................................22

REASONING AND METHODOLGY...............................................................................23

Creating Geocoders & Geocoding CFS...................................................................24Scoring & Evaluating CFS Geocoders.....................................................................25Creating Hot-Spots..................................................................................................28

GEOCODING EVALUATION.........................................................................................30

X, Y Geocoder.........................................................................................................34Incident Address Geocoded.....................................................................................36Geocoder Conclusion..............................................................................................38

HOT-SPOT RESULTS...................................................................................................42

Hot-Spots Outputs...................................................................................................43

CONCLUSION & RECOMMENDATION........................................................................49

Known Project Limitations........................................................................................50Automating Future Analysis.....................................................................................50

5

REFERENCES...............................................................................................................51

Figures Used...........................................................................................................52Biographic Sketch....................................................................................................69

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LIST OF ABBREVIATIONS

(CFS) Call(s) for Service A 911 call or calls, received by Lake County Dispatch, by a citizen(s).

(EMS) Emergency Medical Services

Emergency Service Resources including: Paramedics and Ambulances.

(ESP) Emergency Service Provider

A provider of Emergency Services, including Fire or Medical Emergency Response, to a Call for Service.

(GIS) Geographical Information System

A system of programs capable of storing, processing, and analyzing geographical data.

(ISBA) Inter-local Service Boundary Agreement

A contract signed by the County and another municipal ESP to co-provide EMS.

(ISO) Insurance Service Office A private company that assesses homes based on a variety of factors to determine an appropriate insurance rate.

(LCBCC) Lake County Board of County Commissioners

The legal entity, as defined by Florida legislation, to make decisions for the citizens of Lake County regarding legislation and financial decision making.

(LCFR) Lake County Fire Rescue

The Fire Rescue Department of Lake County Board of County Commissioners charged with providing Fire Rescue support for emergencies within the County.

(LEMS) Lake Emergency Medical Services

A wholly owned not-for-profit corporation by the LCBCC for the purpose of providing quality, community-based Emergency Medical Services to its citizens and visitors. Also the sole receiver and dispatcher of medical and fire Call for Services in Lake County (including municipalities).

(PSAP) Public Safety Answering Point

A call center responsible for answering calls to an emergency telephone number for police, firefighting, and ambulance services; as well as dispatching these emergency services.

(X, Y) X-value , Y-value A method of relaying positional information, given an underlying coordinate system, where X and Y have a given location that corresponds to a place on Earth.

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Abstract Of A Terminal Project Presented To The Graduate School Of The University Of Florida In Partial Fulfillment Of The Requirements For The Degree Of Master Of Urban

And Regional Planning

WHO CALLS: ACCURATELY GEOCODING AND CREATING AHOT-SPOT ANALYSIS OF CALLS FOR SERVICE (CFS) IN LAKE COUNTY

(10/1/2012 – 10/1/2015)

ByBrandon Barnett

December 2015

Chair: Stanley Latimer Cochair: Ilir Bejleri Major: Master of Urban and Regional Planning

Who Calls is an analytical project aimed at identifying the consumers of Lake County’s Emergency Resources including Lake County Fire Rescue, Lake County Emergency Medical Services, and Municipal Fire Agencies. Lake County is located in the heart of Central Florida. Lake County has over 1,000 lakes and rivers, and is home to 14 municipalities each with their own unique downtown environments, people, and cultures (Lake County, 2015). The current challenge of the County’s topography, combined with the rapid expansion of Southern Lake County, have constrained Lake County’s emergency service providers. At the same time, municipalities have rapidly annexed property and changed jurisdictional boundaries. This changes coverage boundaries for both County and City Emergency Service Providers, which makes it harder to collect, quantify, and interpret CFS accurately.

This leaves Emergency Service Providers (ESPs) with a lot of geographic data to keep track of and process, in order to get a comprehensive view of the Calls for Service (CFS) they are responding to. Inter-local Service Boundary Agreements (ISBAs) make responding to CFS easier by sending the closest ESP, instead of responding based on the jurisdictional boundaries responsible for responding to a CFS in that particular area.

This projects hopes to increase the geographic awareness of these ESPs by trying first trying to pin-point where CFS are coming from accurately by evaluating and comparing two geocoded outputs using custom defined parameters. The evaluation processes spatial joined the geocoded outputs to known GIS referenced datasets (streets, address points, and parcel data). Then custom python functions were used to compare the address information of the reference datasets to the address information of the CFS datasets. CFS that fail to spatially relate to reference data will be noted and saved. These failures will then be used to highlight areas that may need to be checked for quality assurance.

After the most accurate georeferenced CFS dataset is identified, the CFS will be processed using ESR’s “Optimized Hot-Spot Analysis” tool. This tool will be used to aggregate and analyze these CFS into Hot-Spots and Cold-Spots. The data will first be mapped and analyzed over 3 year periods: 10/2012 – 10/2013, 10/2013 – 10/2014, and 10/2014 – 10/2015. This will give ESPs an idea of how accurately they can find the location of a CFS, and where areas are experiencing significantly more or less CFS than other areas. This information can then be incorporated into station placement or potentially used for justification for increasing permitting or developing fees for communities that are in these areas.

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INTRODUCTION

GIS Analytical Process

Who Calls aims to first determine where calls for service (CFS) are coming from. The first goal of the project is to determine how accurately the CFS are being located. Then answer the question of “Where are the Hot-Spots (calling 911 significantly more) and Cold-Spots” (calling 911 significantly less) in Lake County?

A “Call for Service” (CFS) is created whenever a resident dials 911 and Emergency Medical Services or Fire Rescue Crews are dispatched. The call types vary and the location of the call dictates the responding agency. Resources can differ based on the type of call and the location:

Vehicle/Apparatus Dispatched: Ambulance, Fire Truck, Etc. The Number of Responders The Responding Agency: County Agencies vs. Municipal Agencies

The important thing is that, for all CFS in the County, Lake Emergency Medical Services (LEMS) handles the dispatching of the resources (County or Municipal). This allows CFS data to be easily aggregated and stored in a centralized database. A Call for Service record would have attributes associated with that CFS including:

Protocol Description (aka “Call Type”) Incident Key Agency Description Response Time Call Origin Time & Date Incident Address Incident City Incident Zip Incident Latitude & Longitude Disposition (aka “Call Status”)

All of this data can be filtered to find groups of CFS that a planner or Fire Chief may find relevant. For instance a Chief might be interested in seeing where the majority of CFS are coming from in the day, compared to in the evening. Or where all of the Calls for Service = “Automatic-Alarm” to see if these CFS are occurring when other types of CFS are occurring. This project hopes to lay the ground work for a future geoprocessing tool that can aggregate and analyze CFS in a manner that is user friendly, as well as statistically accurate.

For this project the CFS data will only be reviewed for locational quality and record redundancy (trying to ensure only unique calls get processed). Other fields will not be checked for integrity. In other words, the location assigned to a specific CFS record will be reviewed in a process outlined later, but the other attributes attached to a

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specific CFS will not be checked for integrity. This project will assume all other fields (Call Type, Date, Time, and Address Information) are accurate.

The location quality or positional accuracy of a CFS will be evaluated by geocoding the CFS dataset in two different ways. This will be explained in great detail later. The first result of geocoding will be done using the X, Y values attached to each CFS record. These X, Y values are populated by a semi-automatic process. CFS that are created using a landline phone (The person calling 911 is doing so by using a landline phone) are automatically assigned an X, Y value to that CFS tabular information coming into the database. CFS created by cell phones force the call taker (person in dispatch receiving 911 calls) to rely on a CAD mapping software to gather information and pin-point a CFS location.

Another output will be geocoded using the address information attached to a CFS. This will be done using a geocoder, also outlined later, that is used in dispatching Lake County Sheriff resources (along with other resources). This geocoder uses a GIS dataset input built by Lake County GIS. This dataset is used in a variety of County applications including: Parcel, Street, and Address mapping. It is updated daily using the Lake County GIS address, street, and parcel information.

CFS will also be checked for redundancy, or duplicate CFS records. CFS have a key field or “Incident_Key”, which is assigned to each CFS. The Incident_Key value is system assigned. Only one CFS per Incident_Key will be mapped. This reduces the chance of any duplicate CFS skewing the data.

The “Call Status” or “Disposition” field will be filtered to show “Completed” CFS only. This will reduce the chances of two agencies responding to the same call, but one agency “Cancelling en Route”. The need to remove redundant CFS records is critical, because the location, or “positional values”, attached to each CFS are used in the Hot-Spot analysis. These values directly affect the results of the analysis.

Other field values associated with each CFS including: date, time, responding agency, address, and response time will be assumed accurate and will not be reviewed due to the complicated process of checking these fields for integrity. Currently the system that stores CFS data does so in a database that is not spatially aware, and because of this limitation spatial questions can’t be answered. In order to answer spatial questions the CFS need to be converted to a database that is spatially aware. This is where GIS and CFS intersect.

First the non-spatial data must be converted to spatial data through a process known as “geocoding”. Geocoding can be done using the X, Y data attached to a particular CFS or the associated address of that same call. Because the validity of the Hot-Spot analysis is tied to the geocoding process, both methods (X, Y data & address data) will be used to convert the CFS to spatial CFS.

The X, Y data is both auto-attached and manually entered for the CFS dataset. It is aggregated in a database built and supported by a 3rd party vendor used by Lake Emergency Medical Services. It also does Hot-Spot analysis as part of a service to assist with CFS analysis. The accuracy of this X, Y data is unknown. Thus the geocoding process behind these X, Y values needs to be evaluated. The analytical tools behind the Hot-Spot analysis are also unknown, and this project hopes to provide something to compare it to.

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Positional accuracy can vary based on the geocoder or geocoding service used (Roongpiboonsopit & Karimi, 2010). Geocoding the CFS using both the address and the X, Y data will allow “Who Calls” to evaluate the geocoding method, and select the most accurate set. Selecting the most accurate spatial set of CFS will ensure the most accurate Hot-Spot mapping results.

Once the data is geocoded it can be spatially analyzed to highlight Hot-Spots, or areas that have significantly higher CFS volumes compared to everywhere else, and Cold-Spots. Cold-Spots are the opposite of Hot-Spots, and they represent areas that are consuming Emergency Services significantly less than anyone else. This is interesting to look at, because this area has a lower risk of having an accident needing EMS.

Finally once the CFS have been processed by: geocoding, aggregating, and statistically analyzed, the Hot and Cold-Spots will be generated and then can be reviewed. Once the Calls for Service are processed in the manner above, the question can potentially be answered about who really is using more services. The “make-up” of these Hot and Cold-Spots could be used to assess where fire-stations could be placed in the future or possibly relocated to better respond to CFS. The analysis could also shed some light on where services aren’t really being used. Why are some people calling for Emergency Services significantly less than others?

Lake County Demographics

Lake County aims to be a “rural, down-to-earth place” (Lake County, 2015). Its geographic boundaries are shown in Fig-1. It is home to an estimated 315,690 people as of 2014, and 75% of those residents own their homes, which is much higher, when compared to the State of Florida average of 67% (United States Census Bureau, 2015). It is important to note the proximity of Lake County to its much larger neighbor Orange County, which lies directly to the East. Lake County’s median value of an “owner-occupied” home is $142,700. Almost half of the citizens in Lake County are at risk for being involved in an emergency or accident. In the County those above the age of 65 (25.7%) or below 18 years of age (20.1%) make up 45.8% of the population. Lake County has 316.6 people per square mile, compared to the State of Florida having 350.6 people per square mile (United States Census Bureau, 2015).

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Fig 1(Google Maps, 2015)

Lake County has grown from a mostly rural community to a County that is well on its way to having a half a million people in the near future. From 2010 to 2014 the population of the County grew from at a rate of 6.3% compared to the State of Florida growing at a rate of 5.8% (United States Census Bureau, 2015). This rapid growth in population, in conjunction along with its high number of lakes, makes placing Fire Stations in Lake County challenging. Lakes are one of the most costly terrains to cross, because bridge construction is costly. Lake County has a lot of shallow, wide lakes. This is especially true in the “central” part or middle of Lake County. This means that moving quickly across the County is difficult, because roads usually go around the lakes.

Retirement communities have been built in increasing volume across the County, which causes an increase in elderly residents and “snow-birds”, or residents that live in Florida during the winter months. These residents are usually temporary and most reside in Lake County during the winter months. This means that there is an increase in older residents, which are not accounted for on Census data, and it could alter the demographics during these winter months. In fact the Census made an extra effort to not count these “snowbirds” on the census, because their residence is temporary (Pew Research, 2015). This means that Lake County could be quite different, demographically, in the winter months with an influx of older residents. This could potentially cause an increase in CFS during the “snowbird” months.

Lake County has a fairly large high-risk population to provide services to when it comes to Emergency Medical and Fire Rescue Services. One quarter of the County’s residents are above 65. This age bracket has a significantly higher death rate compared to other age brackets, with the majority of deaths in this age bracket due to heart disease (27%), Cancer (22%), Chronic lower respiratory diseases (7%), Stroke (6%), Alzheimer’s disease (5%), other (34%) (Minino & Murphy, 2010).

Looking at the 2015 data provided in Fig-2 by Well Florida, an independent nonprofit, it is clear what the leading causes of death in the County are. Cancer, Heart Disease, and Stroke tend to correlate with age (Minino & Murphy, 2010). These types of emergencies are time dependent. It’s been shown that if a person, having a heart attack, gets to the hospital in less than 90 minutes (s)he has a better chance of survival than those who get arrive after 90 minutes. In addition another 20% of the County is

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under the age of 18. The National Center for Health Statistics states that the leading cause of death for people ages 1-24 is “Unintentional Injuries” (38%) (Minino & Murphy, 2010). This includes motor vehicle accidents, poisoning, and falls (Medical University of South Carolina, 2015).

Time plays a major factor in these types of accidents as well. Therefore the need to place stations and responders as close as possible is real. Areas that have a higher risk equate to areas that need EMS more. Response time and station location are both related, especially given Lake County’s topography. If stations are placed away from areas with high volumes of CFS, then getting to those CFS, quickly, becomes a challenge.

In order to respond to CFS quickly the County needs to know where the CFS are coming from, and how their resources are being utilized when responding to them. Emergency Service resources are limited, and return on investment is still a critical concern for tax payers. Resources should be positioned where they can service the areas that have the greatest need, and placed in locations that ensure the shortest response time.

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PROJECT RATIONALE

CFS Analysis is Complicated

Lake County currently has 14 municipality areas, and many have agreements with the County to “jointly” respond to calls. An Inter-local Service Boundary Agreements (ISBAs) between agencies allow agencies to ignore the geographic confinements of their municipal limits and respond to calls where they are the closest agency to the incident. Funding for the County Fire Stations is provided largely by unincorporated residents, while other Municipality Fire Agencies are funded by their respective municipality and residents. Accounting for spending variables is a necessity for any government department. This becomes challenging for departments like LCFR and LEMS. Accounting for each Call for Service (CFS) cost is complex, because accounting for all of the different variables is challenging. Factors include:

Response Time Responding Agency Responding Agency Location Incident Location Incident Type Resources Used Number of Responders ISBA agreement? (not all municipalities have ISBAs) ISBA agreement costs (can vary based on municipality)

Municipalities that choose to sign agreements with the County need to be geographically accounted for when thinking about station placement. Knowing where CFS are coming from also plays a direct role in these agreements. If high numbers of CFS are coming from an area that is near a municipal station, the County could move stations away from this area to better service other areas of the County, knowing that municipal agencies could respond quicker to the majority of these CFS. Showing where municipal stations are in relation to Hot and Cold-Spot areas could aid the County in planning new stations or relocating stations to provide a better level of service.

Tying a cost to every CFS for all the CFS in Lake County would mean aggregating every call in dispatch and tapping into individual municipal pay roll data and cost data. The feasibility of such an accounting project would be low. The jurisdictional boundary lines change all the time. Municipalities can annex properties changing municipal jurisdictions. The street intersection, and parcels next to those intersections, can reside in different jurisdictional boundary lines. This means that a situation can arise where different agencies respond depending on whether or not the accident occurred in the intersection, or at the gas station on the corner. ISBAs eliminate these issues, but add other planning aspects when it comes to station location. If the station existed before the agreement, then stations may need to be relocated. Relocation can be cost prohibitive during lean economic times, which adds another challenge in providing EMS. The ability to respond to CFS, quickly, benefits everyone.

Responding to CFS quicker equates to a better ISO (Insurance Service Office) rating, which can result in lower home insurance rates for Lake County citizens (Stanford L. , 2014). The ISO rating is like golf; a lower rating is given for better

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performance on a scale from 1 - 10. In a recent Daily Commercial article it stated that “Lake County Fire Rescue will have its fire insurance rating evaluated for the first time in 10 years, and fire department officials hope for a better rating, which would result in potentially lower homeowner insurance rates for residents.” This directly ties the results of these departments to cost of living in Lake County. If the level of service for LCFR and LEMS increase and the ISO ratings reduce, then residents are looking at a lower cost of living in Lake County.

Understanding and accounting for all of the geographic factors and potential variables requires a high degree of GIS understanding, which is why Who Calls can be valuable to both County and Municipal Agencies.

The County was given an ISO rating of “6” back in 2005 and the rural areas had an ISO rating of “9” (Stanford L. , 2014). Since the 2005 rating the County has replaced 30 vehicles, purchased a 6,000 gallon tanker truck (to better service the rural areas), and have added three new fire stations. In addition to that, “six volunteer stations were converted to full-time stations.” (Stanford L. , 2014). Hopefully these additions will improve the County’s score and reduce the ISO rating, especially in the rural areas. The Hot-Spot maps could also serve as justification to relocate stations in an attempt to further lower the ISO ratings. At the time of publication the new ISO ratings had not been released.

Return on Investment

Once Who Calls is complete, Emergency Service Providers will know where the “Hot-Spots & Cold-Spots” are occurring in Calls for Service, and how they could better utilize the limited amount of resources they have. LCFR received no additional funding for its 2015-2016 fiscal budget starting in October (D'Marko, 2015). This project could potentially relate to how well the County can interpret and plan for the majority of its CFS, and hopefully lead to a better ISO rating and subsequent lower home insurance rate for all residents. The News13 article states that the County recently approved an institution fire fee increase based on an internal study that showed an increase for CFS to institutions (D'Marko, 2015). In the same article even a LCBCC commissioner states “In reality you have certain users within that category who really use the bulk of the services, so I think we need to look at that more closely so it’s a fairer fee”. Knowing who is consuming services is critical to providing emergency services. Knowing where and when a CFS occurs can help ESP plan for the next CFS.

There are still concerns that the amount of Fire Rescue personnel maybe too low, because “Staffing makes up 15% of the ISO.” (Stanford L. , 2014). If management knew how Calls for Service differed throughout the day, Lake County Fire Rescue could move assets based on time. If, for example, there are Hot-Spots in the South – East area of the County during the day, and Hot-Spots in North – West area of the County at night, Fire Rescue could move assets based on time to better respond. This maximizes the return on investment of the already limited number of Fire Rescue personnel. This is especially true when thinking about communities that have a high amount of “snow-birds”, or seasonal winter residents. Many of these residents are older than 55, which places them in the high risk bracket. These seasonal residents are not counted in the census data, which makes planning for them harder. Their locations can change depending upon development, relocation, etc. Therefore trying to account and plan for

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“where” they are located can be challenging. If the Hot-Spots are significantly correlated with retirement communities, or communities that have >55 age restrictions, then charging more for building these types of communities could be justifiable and make equitable sense among tax payers.

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LITERATURE REVIEW

Geocoding

In order to increase the validity of Who Calls, both the methodology for aggregating (geocoding) and analyzing (Hot-Spot analysis) the Calls for Service data will be reviewed. In addition to this, other examples of similar techniques applied for different purposes will be reviewed. This should better demonstrate the feasibility of such techniques and their validity when applied to real world data.

Aggregating data, spatially, is not new and the technique for doing so is now largely refined. As stated in the Comparative Evaluation and Analysis of Online Geocoding Services, “In the information age, spatial information is considered as one of the most essential value-added pieces of information” (Roongpiboonsopit & Karimi, 2010), knowing where is a powerful thing. Location is usually in the form of X (Latitude) and Y (Longitude). The X and Y (X, Y) values are based on a grid system that tries to create equal sized squares across the entire globe, otherwise known as a projection. All projections have a 0, 0 or point of origin. When defining a location in terms of latitude and longitude (X, Y) is usually where the Prime Meridian and Equator intersect.

In summation a projection, with a defined coordinate system, is used to provide known locations (X, Y) and geocoding uses these known locations (X, Y) to known values (addresses, business names, people, etc.) and compares them to a given value. Projections vary based on the scale of the map as do coordinate systems. Since locational accuracy depends in some ways, on the projection being used, mapping larger (continents) or smaller (states) areas means using different projections in order to maintain geographic accuracy. This means using services aimed at looking at a “world” scale may be inaccurate for data being analyzed at a smaller scale.

Geocoding takes a given value and compares it to set of known locations based on known values. If the given value matches the known value (gV = kV) the given value is given the known location of the known value (gVxy = kVxy). In the real world this translates to using the example address of “100 Main Street Tavares, Florida 32778” and getting directions to that physical building that has this address. The address is just random text, but when geocoded that text, “100 Main Street...” is matched to a location, or physical location on the planet, and the location returned when using an address can vary based on the service (Google, Yahoo, Bing, ESRI, etc.) used to geocode that text information. The service used in this process is called a “geocoder”. A geocoder “geocodes” text information (315 Main Street) in a process known as “geocoding” to return a location (physical place on the planet).

These known values, and subsequent known locations, can be related to almost anything. They can be the known locations for addresses, business names, people names, streets, etc. A known value and known location table make up a “geocoder”. Geocoders store X, Y values. These stored values are based on the data at a given time, and they must be updated daily in order to have the most current data. This is why the geocoder used matters. The X, Y values stored in these geocoders are based on a specific projection system, and the output data should match the geocoder’s projection system to ensure location (positional) accuracy.

In an effort to summarize geocoding:

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A geocoder is a tool used to take a given value and compare it to known values, which gives the given value, which is the location.

o They use lookup tables that have text information and subsequent location information

A “composite geocoder” is created when several geocoders (each having unique lookup tables) are bundled together. Each geocoder is ranked in an effort to match the most common thing looked up.

Geocoders can be published as “geocoding services”, which make the geocoder available to anyone who wants to consume that service, and they can be bundled up so that one given value can be compared to several known lookup tables at once.

In very simplistic terms this is what Google Maps is, a gigantic “composite geocoder” with millions of lookup tables, each with an X, Y value for a random text values: addresses, business names, or key values (gas, food, clothes, etc.)

A geocoder can be evaluated based on its match rate, positional accuracy, and repeatability (Roongpiboonsopit & Karimi, 2010). Geocoders can have different accuracies, and because positional accuracy is a critical component in the spatial analysis process of this project, selecting an accurate geocoder will result in the most accurate Hot-Spot analysis result.

Hot-Spot Analysis

Hot-Spot mapping involves analyzing the location and associated value of a dataset in a specific region. It was inspired by Getis and Ord, who in 1992 introduced a suite of statistical spatial tools denoted G. These tools were used for assessing spatial correlation for a variable without any idea of where that may correlation may exist (Fischer & Nijkamp, 2013). It assumes that spatial autocorrelation, or self-variable correlation, exists. If autocorrelation exists then areas that are clustered could be correlated (Fischer & Nijkamp, 2013). Testing for spatial autocorrelation is much like testing to see if the variable is dependent, but in this case the variable would be dependent on the region. Spatial correlation asks “Does the variable change in value based on the location within the region”.

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Moran’s I is the technique that is the most popular method for testing for global autocorrelation. Fig-3 shows the equation used to test for global autocorrelation in this analysis. It asks the question, “Does the variable correlate with region?”, and if the variable is dependent on the region then correlation could exist between the region and the variable in question. If global correlation exists, then the variable in question is already correlated to the region.

Variables can be correlated to the region in clustered or dispersed manner. A good way to highlight this is: lower elevation areas are more likely to have an increased risk for flooding. There is a geographic correlation to the dataset. If we were looking at the damage to homes during a flood, the resulting “Hot-Spots” would be clustered around these low elevations and the data would be skewed and not accurate.

Hot-Spot mapping assumes that there is no global spatial autocorrelation, but instead local autocorrelation. In other words globally no correlation exists between the region and the variable, however at a local scale (neighborhood) there is correlation between the variable and the neighborhood. Hot-Spot mapping aims to show this correlation by mapping where the variable is unusually present (or absent) in some areas compared to the region as a whole (Fischer & Nijkamp, 2013). Hot-Spot correlation asks the question: “Does the variable change in greater/lesser value when compared to the average change, within the region, for that variable?”

Fig-4 shows the equation used to analyze the Calls for Service for “Hot-Spots”. The Getis-Ord Statistic (G) statistic uses the null hypothesis that all areas of a region are equally likely to show a given phenomenon. Areas that show localized clustering of that phenomena represent areas that reject the null hypothesis and thus represent areas that are more (or less) likely to have that phenomena present.

Dr. John Snow’s famous mapping of Cholera, in 1854, represents the core concept behind Hot-Spots well. If the chance of having symptoms from a given illness are based on something besides where patients are located then all areas have an equal chance of having a symptomatic patient. If one area has a higher prevalence of

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symptoms, with respect to the region as a whole, then this area represents a higher chance of being the source of symptoms.

Dr. Snow theorized that the Cholera virus was spread through contaminated water. If this was true, then all the patients would have gotten their water from the same contaminated source(s). Finding areas that have higher concentrations of sick people, means the sickness has “been” there longer. This means the areas with the highest concentrations of sick people are more likely to contain the source of sickness.

Dr. Snow’s map, shown in Fig-5, showed areas where patients were symptomatic in relation to the water pump locations. A high number of symptomatic patients near one “foul” well supported Snow’s theory that Cholera was linked to water quality, and the eventual discovery of how Cholera spreads through contaminated water. The need to represent data geographically to find

solutions is real. Adding spatial components to data can provide insight that otherwise would be difficult to obtain. Analyzing data spatially can relate the data based on specific values (time, type, or agency) to specific regions, because sometimes the location makes all the difference.

The Getis-Ord Statistic method utilized in this paper has also been utilized to find areas of concern by the Texas Department of Transportation (Goodwin, Schoby, & Council, 2014) for solving resource allocation problems regarding improvement projects, and by North Dakota State University using geostatistical analysis to detect traffic accident Hot-Spots. Traffic accidents can be filtered in the CFS data, and this provides another potential starting point for additional analysis of the CFS data.

In the North Dakota study, the scale, or the geographical boundaries of the study area was key. The study noted that when looking at the total number of traffic incidents, North Dakota ranked low. When looking just at North Dakota however it was clear that specific counties within North Dakota were experiencing far higher rates of traffic fatalities, compared to other counties. The scale or extent of the Hot-Spot analysis is an important factor to consider when doing any analysis that involves the creation of “Hot-Spots”. The study also notes how important it is to look statistically “into” the data being used for generating the Hot-Spot results, especially concerning data distribution and skewed data (Molla, Lee, & Stone, 2014). The “Aggregation Distance” or distance when two points are “aggregated” together is critical. A distance that is too-large will over generalize the analysis, because it will aggregate too many points into one average value. A distance that is too-small will skew the analysis, because spatially related

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points will not be averaged together. The “aggregation distance” is based on the distance between each record in the input dataset.

GIS & Other Governmental Agencies

Problems involving resource allocation, disease, pollution, crime, and accidents have all been studied using spatial statistics. GIS allows multiple variables to be aggregated in space. This links an object and a location together. Linking records with a location or positioning records allows records to be analyzed using spatial tools. The North Dakota State University mentioned in the previous section, shows using GIS to look at other “human” phenomena including traffic accidents.

The Texas Department of Transportation used GIS to solve a resource allocation problem. Resource allocation problems occur in business and governments all the time. Resource allocation means having resources and distributing resources to solve or resolve incidents. A resource allocation problem occurs if the number of incidents exceeds the amount of resources available. Therefore the resources available must be used in a manner that solves the greatest number of incidents. TDOT’s analysis focused on teenage driver accidents. TDOT looked at crash data from 2006-2009 and created Hot-Spots from so called “blackspots”, or intersections with high accident rates (Goodwin, Schoby, & Council, 2014). The TDOT notes the difficulty of determining where to act and states “when a city or area reports numerous accidents all over the city, it becomes difficult to determine if there is any significance or pattern to the accidents”. Traffic accidents are caused by a complex web of factors. Aggregating traffic accident data and employing GIS allows the data to be viewed from a spatial perspective where things like road capacity, time of day, type of accident and other factors can be normalized, and researchers can view the data on an “equal” playing field. GIS allows agencies to scale up their perspective and look at the data from a variety of viewpoints and scales.

First TDOT aggregated the crashes at intersection points within Houston, Texas. Then the aggregated points were analyzed using ESRI’s Getis Ord Gi star statistical tool, which looks for high and low Z-scores to produce areas of high correlation / probability (Hot-Spots) and areas of low correlation / probability (Cold-Spots). This tool looks at features from a neighborhood or local perspective. It computes an expected local sum by looking at all the features in the region.

An individual feature with a high Z-score does not necessarily represent a Hot-Spot. A Hot-Spot requires a cluster or group of features all with high Z-scores as well, as having a local sum that differs significantly from the expected local sum. It computes a Z-score (Gi) for a local region compared to an expected value computed from all the features in the region (n).

Texas Department of Transportation found several things. First the amount of accidents had decreased from 2006 – 2009 by 21% (Goodwin, Schoby, & Council, 2014). Second TDOT found they had several Hot-Spots in the Houston area. Lastly TDOT noted that Hot-Spots changed locations as time progressed. Areas that were Hot-Spots in 2006 were Cold-Spots in 2009, indicating that the underlying landscape responsible for these Hot-Spots had changed (Goodwin, Schoby, & Council, 2014). Districts containing clubs, bars, and other businesses that have a high prevalence of

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alcohol and younger age individuals create a higher risk population for traffic accidents. As these districts move across the region they move the risk population, which have a higher likelihood of being involved in an accident. It’s important for agencies to be aware of geographic landscape they are deemed responsible for, and as these boundaries change so too must the agencies resources.

GIS & Cost of Living

The ability to respond to a Call for Service with the right resources and in the right time frame affects the County’s ISO (Insurance Service Office) rating, which can impact how much home owners pay for insurance. This directly contributes to the cost of living in a specific city or county. The Insurance Service Office provides information to “establish appropriate fire insurance premiums for residential and commercial properties”.

ISO evaluate agencies on a variety of factors including: the fire department, the water department, equipment, staffing, training, facilities, inspection of fire hydrants, flow testing hydrants, community programs, and other factors (Yerace, 2013). The score or ISO rating is on a 1 to 10 scale, with 10 being the worst score. The score is broken down into 3 categories. 50% comes from the Fire Company evaluation, 40% on the water-delivery system, and 10% for alarms and community outreach (Yerace, 2013).

The City of Yatesville, in Georgia, recently lowered their ISO rating from a 6.9 to a 4.9. This translated into a $47/month difference for home-owners in the area starting March 1, 2015 (Stanford L. , 2014). One major factor is whether or not there is a fire hydrant within 1,000 feet of the home and that the insurance agency has an easy way to access this information (Stanford L. , 2014). If Lake County could improve their ISO rating, they could directly contribute to lowering the cost of being a citizen of Lake County. Lowering their ISO rating would equate to good press and a great morale booster for all first responders.

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REASONING AND METHODOLGY

To assess whether or not certain demographic categories are consuming more Emergency Services Who Calls will look at:

1. Where calls are coming from (geocoding the CFS)2. Where calls are concentrated (Hot-Spot Analysis of the CFS) and not

concentrated (Cold-Spot Analysis of the CFS)If CFS for Emergency Services are distributed evenly across the County, then the station location is not important, because every part of the County has an equal chance of having an accident needing Emergency medical Services (EMS)

If CFS are not distributed evenly, why? The data used in this analysis is derived from the Calls for Service database. A Call for Service is created when a citizen calls 911 and reports an emergency that requires: Fire, Police, or Medical Services. Because police CFS data is housed in a secure environment the data will not be processed in this project. Only Fire and Medical CFS data will be processed. A car accident requires both medical and fire rescue personnel. This analysis will not distinguish between the type of Emergency Service Provider (Fire / Medical), but instead look at whether the CFS originated within a “Hot-Spot or Cold-Spot”.

When a Call for Service (CFS) is recorded in the database the CFS information contains several fields including:

Address Information Time / Date of CFS Response Time Responding Agency Protocol Description Number of Responders Incident Latitude / Longitude Incident Address Information

Georeferencing the data is the hardest part of this task as the address information attached to the CFS data is error prone. The address information is usually provided by the caller, in a time of distress, in a place they are not familiar with. Getting an accurate location for each CFS is critical, to providing an accurate Hot-Spot map result. If the input locations being statistically evaluated are flawed, then the whole analysis is flawed.

The back-end process used to populate the location information into the database is complex, usually using an unknown geocoder, and may need code modifications from the vendor to work with other geocoders or geocoding services. Code modifications are usually not cost-feasible, because of costly vendor fees. Currently the CFS data has X, Y data attached to all of the CFS in the dataset being analyzed, and is provided by a geocoding service used by the vendor and dispatcher. But in LEMS’s case the vendor does offer the ability to consume a custom geocoding service. Comparing the accuracy of the vendor geocoder vs. the County GIS geocoder could show that the current process is not as accurate as assumed. Geocoding the CFS by the X, Y values is far easier, however it makes the assumption that the geocoding service used to obtain the values is accurate.

Getting a location for a CFS or “geocoding” can occur in two ways. The first method is an automated process that generates the X, Y data based on the caller’s

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GPS unit in the device they are calling from. In this case landlines provide the most accurate X, Y data. If the caller is calling from a cell phone the accuracy depends on signal strength and the device’s internal GPS hardware. The alternative is that the system takes the address data, entered by the dispatcher, and uses a geolocator to determine the X, Y data. A geolocator can be built using a variety of GIS layers including: address points, streets, parcels, etc. The geolocator has known X, Y values for the GIS layers that were used to build it, and because of this, the process is only as accurate as the layers that went into building the geolocator. The construction of geocoders can be error prone, because it requires a lot of highly accurate GIS input layers.

If the GIS data point attached to 315 Main Street is in the wrong location then the resulting X, Y output for this point, using the method above, will be inaccurate. Another thing to consider is what the GIS input data represents vs. what the data being mapped represents. The GIS input data could be using the City jurisdictional boundaries to define the City values for addresses, whereas the address information entered by the dispatcher is likely based on the postal City limits. This discrepancy would throw off mapping any addresses that have a city value outside of the City jurisdictional limits.

If the data is geocoded incorrectly then the resulting Hot-Spot analysis will be flawed. Then any changes based of this analysis would then also be flawed. This is why the positional accuracy of the input dataset is so critical.

Creating Geocoders & Geocoding CFS

CFS cannot be tested in the traditional manner for positional accuracy, because this would have to be done internally by an agency like LEMS. As a CFS occurred, the data for that individual CFS would have to be compared to the actual location on the ground for that CFS to see if the database record matches the real incident location. Such a process is outside the scope of this project.

Instead two georeferenced point feature classes will be created and assessed for accuracy (2 outputs one per geocoding technique). One output will be built from the results of geocoding the data using the X, Y data already attached to the CFS by the vendor/dispatcher process (using an unknown geocoder and human inputted data). The other will be derived from geocoding the addresses in each CFS record and using a custom geocoder built by indexing the streets, street intersections, and address points into one geocoder (using the inputted citizen information as taken by the dispatcher).

Lake County GIS builds a geocoding service which uses over 180,000 address points, streets, street intersections, and Alternate Keys (Parcel Identifier) as inputs. This geolocator is also used by the Lake County Sheriff’s Office in their dispatching environment. It will be used in geocoding the CFS using the address information provided in the CFS data.

The County geolocator is built in a somewhat redundant way, this maximizes the potential match pool. Increasing the match pool increases the number of records a given input value could match to. For instance, a street may have an alias. Generating the address point with the regular street name and again with the alias street name ensures that the address point values match all possible combinations. The same is true for street suffixes. If the person using the geocoder is typing in “Main Street” when the data was built using “Main St” then the match may not occur and no location

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information will be returned. Record redundancy is a good thing when it comes to geocoders, as long as the redundant records return the correct location.

The CFS dataset being processed in this project has over 100,000 records spread across ~4 years (1,093 days). The address information for each CFS is spread across several fields and is combined into one field “I_Fadd”. This field was created in Excel and populated by concatenating the individual address fields. The Disposition or “Call Status” field was also preprocessed in Excel to remove duplicate instances of “Incident_Keys” or the key field used to identify a unique CFS.

Geocoding using the already attached X, Y values is fairly straight forward using ESRI’s ArcMap software. It takes about a minute for the software to geocode the input points. LEMS provided me the projection system information to ensure that the geocoding projection matched the input data X, Y projection. Geocoding in this manner required the user to specify the latitude and longitude field values, which is attached to all the CFS records in the dataset under the fields “IncidentLat” and “IncidentLong”. A CFS record will be considered accurate if the “I_Fadd” in some way matches the GIS Features: Address, Street, or Parcel information. The location given to these CFS records is based on the X, Y values for each record, which are based on the landline match or call taker inputted data.

The second geocoded result comes from the CFS dataset that is created by geocoding using the “I_Fadd” field, which combined the Number, Street, City, and Zip code information into a “Full Address” field, for each CFS. This field was used to geocode the CFS using the geocoder built by Lake County GIS. This geocoder uses the Lake County GIS maintained data as the input data, and is rebuilt monthly to ensure that the geocoder has up-to-date information. A CFS geocoded in this manner is assigned a location based on the “I_Fadd” field, which is based on the caller address information taken by the dispatcher.

Scoring & Evaluating CFS Geocoders

To determine the positional accuracy of each resulting geocoded output, both outputs will be clipped to the County Boundary and then evaluated. In order to evaluate the two geocoding outputs a “score” will be assigned to each CFS. This score will be based on the address, street, and parcel information. A new field will be added for each of these comparisons. Each CFS will have a “StreetScore”, “ParcelScore”, and “AddressPointScore” field added to it. These fields will be populated based on where the geocoded CFS falls by spatially joining each geocoded result to each of the GIS layers. This spatially relates the geocoded CFS to the Street, Parcel, and Address Point features, which allows the attributes for these features to be “pinned” to the CFS record. If a CFS lies in an intersection, the CFS will be joined to both streets in that intersection. This is called a one-to-many join. This type of join is used for joining the CFS to as much input GIS data as possible, and using this type of join actually increases the number of records. If a CFS record is located in an intersection it will spatially joined to both streets, creating two records (one for each street join) each with the same Incident_Key value. While it may increase the dataset using one-to-many joins, the CFS can be reduced back to the original count later using the Incident_Key field.

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Scoring the data required the geocoded result and County GIS data be spatially joined, which allowed for the fields to be compared. First the County’s address point feature class was buffered using a 50 foot buffer and then dissolved based on the “Full Address” field. This field will be used in comparing the geocoded CFS to the actual address point information. If the geocoded result matches the address point information then a score of “3” is given in the “AddressScore” field.

The Lake County parcel layer also contains address information and will be dissolved based on the “Property Address” field. This “PropertyAddress” field will be used to evaluate the geocoding accuracy to the parcel information maintained by the Lake County Property Appraiser. Since parcels can be quite large, a score of “1” is given to CFS if the “IncidentAddress” matches the “PropertyAddress”.

Finally the Lake County GIS Streets layer will be buffered 50 feet and dissolved based on “Base Street Name”. This will create a polygon along the streets that will catch CFS that are occurring in intersections and along streets. CFS that geocode correctly to the street are given a score of “2”.

This will create a “list” of fields: AddressScore, PropertyScore, and StreetScore that the CFS address can be compared to and evaluated by. Again the geocoder gets “1” point if the CFS Full Address is in the Property Appraiser “Property Address” field, “2” points if the street “Base Name” is in the CFS “Full Address” field, and “3” points if the CFS “Full Address” is equal to the “Full Address” of the Lake County Address Point data. Then all the scores will be added together and the “scores” of each geocoder can be compared.

The function used to calculate the “Street” score is shown above in Fig 6. If the Street, which is the BaseStreet Name, is in the Incident Address (I_Fadd) then the score is equal to 2. An example of using this function would be an intersection accident. The .lower() ensures that the results are being compared in lowercase. An example of this would be a CFS record generated having the address of “East Apple Street & South Minneola Street”. The IncidentAddress, in the function above, would be “east apple street & south minneola street”. If the CFS geocoded correctly then the Street value would be “apple” or “minneola”.The BaseStreet Name of “apple” is in “east apple street & south minneola street” and the resulting CFS would be given a score of “2” in the Score Street Field. This function also tries to score matches that match the Street alias using the second IF statement.

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If the address in the CFS record is “315 W Main Street” and the address point “Full Address” is “315 West Main Street” then it is a valid match, and should be scored as a match. Fig-7, below, shows the address point score function used to try and ensure scoring integrity when comparing the Incident Address value and the Address Point “Full Address” value. The function also tries to account for the spelling differences when it comes to directional differences in spelling. 315 W Main Street and 315 West

Main St are the same. The second IF statement will take the first item (the address number) “315” and the second to the last value or “Main”. If Both 315 and Main are in the incident address then a score of 3 will be assigned in the address score field.

A custom function was written for the PA score field as well. Fig-8, above, shows the Property Appraiser scoring function. Again, great care was taken to try and ensure valid matches were scored. The property appraiser “Full Address” field had a series of spaces between the street and city information, and therefore the split function was

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Fig 8 (Brandon Barnett, 2015)

used to try and split the field and reconstruct it to ensure match validity and score integrity. The same “reconstruction” idea was done to deal with directional spelling issues in both the Property Appraiser address information and incident address information.

Once the CFS are spatially joined, and the address information scored, the dataset can be reduced back to unique CFS using the “Incident_Key” field. The Tax Parcel, Address Point, and Street layers address scoring information can then be aggregated and evaluated. The X, Y geocoded output has an address and the GIS geocoded output also has an address value. In this manner, each geocoded dataset can be checked for accuracy to some degree by comparing the geocoded location to the incident address information. CFS that are geocoded in the right of way or in the road way would, theoretically, fall outside individual parcel lines. Thus this is where the Street buffer will be used to evaluate the intersection geocoding. Finally CFS that geocode accurately to the County’s Address Point layer show a very high degree of accuracy and therefore get the “highest” reward with a score value of 3. Once the best georefencing method is selected the next step will be to create Hot-Spots.

Creating Hot-Spots

Once the CFS have been geocoded, the dataset is now spatially aware using the most accurate method possible. Each record in the dataset has locational information, which means that GIS analysis can be performed on the CFS dataset. Hot-Spot mapping is a process that first aggregates the data into “polygons” or boundary boxes so that it can “count” the number of incidents in each of these polygons. Then the software averages out the “count per polygon” and highlights polygons that exceed this average and fall below this average. The polygons are then combined into “Hot-Spots” and “Called Spots”.

The CFS must be aggregated in the same manner across the region for the results to be accurate. The size of the polygons usually depend on the amount of input data. In this case there are over 100,000 individual records, each with its own unique locational information. The size of the polygons can be smaller because the dataset is so large. The smaller the dataset the larger the polygons. The larger the polygons the more “generalized” the analysis is.

After the CFS are aggregated the CFS can be counted per polygon and a baseline average of CFS can be established for how the number of CFS per polygon compares throughout the County. This baseline is the “average” and any polygon count that deviates significantly from that average represents a hot or cold polygon. Geographic features like lakes can drastically alter this baseline because no CFS are originating from lakes.

Finally once the CFS are aggregated correctly and evaluated using the averaging technique discussed above and in the Literature Review section, the Hot-Spot and Cold-Spot outputs can be generated and the analysis is complete. The geocoder evaluation will provide an idea of how accurate the input CFS locational values are. This locational accuracy will also provide an idea of how accurate the Hot-Spot results are.

In this analysis the “Optimized Hot-Spot Analysis Tool” will be used to generate Hot-Spots. This is an ESRI tool, which is found in the Spatial Statistics Toolbox, that

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takes an input layer and automatically analyzes the input data, adjusts the parameters of the analysis, and generates the corresponding Hot-Spot output.

The tool automatically analyzes the data to set the aggregation parameters. The aggregation parameters are largely defined by the number and geographical position of the input data. There are several methods that the tool can use to aggregate the CFS data, and each method has a minimum number of Incident records. This analysis will always use over 10,000 records and well exceeds the required minimum number of records needed. Aggregating the CFS could have been accomplished by either:

1. Creating Fishnet Polygons (polygons of equal size) based on the positional values of each CFS, and counting each CFS within them. If there is no CFS record within a polygon it is omitted and the Hot-Spot analysis proceeds with aggregation.

2. Snapping nearby Incidents together and creating weighted point features.

This analysis will use the second method, because the analysis accounts for small geographic differences in geocoding and “weighs” these points together (ESRI, 2015). This will help to account for the geocoding variation that exists within the CFS dataset. This method was also used in the TDOT method for “aggregating” incidents together and was noted in the North Dakota study as being used in other traffic accident studies (Molla, Lee, & Stone, 2014).

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GEOCODING EVALUATION

The CFS data was obtained in four concurrent excel files from LEMS. One file for each year, starting on October 1st, 2012 and ending on October 1st, 2015. The first step was to ensure consistency in the data. CFS have a field that signifies call “disposition” or call status. The call disposition field can have 16 different values, representing different “Call Statuses”.

This analysis will focus on the 117,763 CFS that are “Complete” and “unique” over these years. This ensures that the Hot-Spot analysis does not take into account CFS that were aborted or cancelled. It also helps to reduce redundancy in the CFS dataset, because if multiple agencies responded to the same CFS record then that one accident would be reflected twice in the analysis.

This would skew the data towards areas where multiple agencies responded to the same CFS. A snippet of CFS data is shown in Fig-9, above. All of the field columns are shown below, along with a field description:

Date – Date of the CFS Disposition – the “Call Status” IKey – the “Incident_Key” signifying a unique CFS Iaddress – the result of Trim(Incident Address) IncidentAd – Incident Address IncidentCi – Incident City IncidentLa – Incident Latitude IncidentLo or Incident Longitude IncidentZI or Incident ZIP I_Fadd – The concatenated “Full Address” for a CFS ProtocolDe – The “Call Type” Unit_Displ – The unit code that was sent to a CFS CFSYEAR – The year the CFS occurred

The four excel files were pre-processed in Microsoft Excel. The field I_Fadd was created and calculated using Excel’s text concatenate formula to combine the Iaddress, IncidentCity, and IncidentZIP. Excel’s remove duplicate feature was also used to remove duplicate Incident Key’s (Ikey). This generated the CFS input dataset consisting of 118,153 “completed & unique” CFS from 10/1/2012 – 10/1/2015. This dataset was then brought into ArcMap as a .csv file.

The way to ensure continuity processing the data is to use ArcMap’s model builder to layout the analytical process used to analyze the data. Fig-10, below, shows

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the CFS input dataset summary. This table represents all the CFS in the original CFS dataset dump. It does not represent the input dataset going into the Hot-Spot analysis.

The two datasets will be described in a similar manner. First each geocoder will be discussed and any notable information will be disseminated. The process of using a one-to-many spatial join creates “redundant” CFS records with each spatial join, as outlined above. When a geocoded point “intersects” two different features each point intersection, per feature, creates a unique output.

The process for evaluating each geocoder, which is outlined above, uses three spatial joins. The first was to the Dissolved Tax Parcels layer. This layer has all of the tax parcels in Lake County, and their associated “property address” as recorded by the Lake County Property Appraiser. The second spatial join was to the Streets buffer. This feature was used to relate CFS to known streets and intersections. The final spatial join was to the Address Point buffer, which is a 50 foot buffer around the Address Point feature class used by Lake County GIS. Each spatial join increases the amount of CFS records, but the Incident_Key can be used to determine how many unique CFS actually became spatially to the CFS.

For example, if Incident_Key 112 geocoded to an intersection, it would be spatially joined to both streets of the intersection, but both “joins” (one per street) have the same Incident_Key of 112. When they are summarized by Incident_Key these duplicates can be “combined” back into unique CFS.

These “summarized” CFS represent the number of unique CFS that successfully geocoded to known features, which are the Lake County GIS data (Address Points, Streets, and Parcel data). These “successful” CFS translate to having the correct data for each incident, and positioning that data on the planet correctly. This adds validity to the Hot-Spot analysis, because the input location data is accurate. It also will add validity to the current system if the CFS have a high positional accuracy. “Knowing” where CFS are occurring from and recording the incident is the purpose of the dispatching system, and having a high positional accuracy means the system is working efficiently.

These successful CFS represent how many unique CFS each geocoder was able to join to. The “Summary Statistics” tool, shown above in Fig-11, can be used to get the max score per Incident_Key. The Max Total Score (calculated by adding all the scores,

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per Incident Key, together and getting the highest value for each Incident_Key). This translates to getting the highest match value per Incident Key. If the CFS occurred in an apartment complex, then that CFS may spatially join to multiple address points (one address per apartment building). One may be correct, but others may not.

Such an

incident is shown above in Fig-12. Here a shopping center is shown, each circle represents the 50 foot buffer per address point in the shopping center. Fig-12 shows an incident where a CFS becomes spatially related to 18 address points. Each circle has its own unique address, because it represents a business within the shopping center. The summarize tool gets the highest scoring value per Incident Key (or Ikey) for these 18 relations, and the resulting output is shown below in Fig-13.

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The resulting table shows all the unique Ikey values (or Incident_Key), the Frequency of Ikey (how many times each Ikey value occurred) and the Max_TotalScore. Using the same example from above this CFS, represented by Ikey 36680, was spatially related to 18 address points. The Ikey 36680 Max score was 3 indicating that 1 out of the 18 matches had the correct address information (as evaluated by the function in Fig-7). The GIS features are shown in Fig-14, and it is clear that this particular CFS failed to have the correct Parcel Address information, because it was within the parcel boundaries but has a score of Max Score of 3. This means that the physical address Lake County Property Appraiser has for this particular parcel does not match the CFS incident address information.

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This is not uncommon because the property address for a shopping center differs from the individual addresses of each tenant within the shopping center. This CFS incident failed to geocode (be located) inside the street boundaries to be evaluated for a street match.

The Max_TotalScore represents the highest score (after summing the AddressScore, PAScore, and StreetScore fields) for each Ikey or “Incident Key” value. The number of CFS that successfully spatially joined to CFS, or CFS that have a Max_TotalScore > 0, along with the sum of the “Max_TotalScore” values will be used in the determining which geocoder is more accurate. A higher number of successful joins indicates better positional accuracy in relation to known positions (Lake County GIS data).

In summation, CFS that have a match value greater than 0 will be counted. This number, known as the match rate, will be used to evalute the geocoders. The Total Score precentage of the CFS (summed score of all the “total match scores”) will also be used to evaluate the geocoders. Note that each geocoder will be scored out of “117,763” records. This is the number of CFS that are within Lake County.

X, Y Geocoder

Geocoding using the X, Y values, which were produced by an unknown geocoder, is straight forward using ESRI’s ArcMap. Some CFS records had X, Y values that were more accurate than other CFS records. The number of decimal places in an X or Y value increases the accuracy of that value significantly. For instance the difference between .1 and .01 in decimal degrees is like the difference between a City sized area and a town-sized area. Many of the records had decimal places that were out 9 places (0.000000001), indicating a high level of accuracy while others only had 4-6 decimal places. 4-6 decimal places still indicates a high degree of accuracy, but 9-10 decimal places, which many records in the CFS database had, represents the ability to distinguish between each end of a table. This indicates that however these values are being acquired, they have a high degree of positional accuracy information.

This geocoding output also had records that fell outside of the County boundary. This is not surprising because the County does have mutual aid agreements with some of the surrounding Counties. This means that the geocoding service provided by the vendor has GIS data that is outside of the Lake County boundary making this geocoding

service aware of “more” area than the County GIS geocoding service. The County uses only Lake County GIS data input layer limiting the area of focus to Lake County only.

After clipping the X, Y geocoded CFS, 117,763 unique and “Complete” CFS were within Lake County. This is the total number of CFS in the dataset. Fig-15, above, shows the geocoded result before the clip process occurred. 115,032 out of the 117,763 geocoded, CFS using the X, Y positional information, successfully. This gives the X, Y positional information, populated via

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system-generated (landline lookup) and user-generated (dispatcher lookup) information, an impressive match rate of 97.68%.

A summary of the processed X, Y geocoded result is shown in Fig-16, showing that 2,731 CFS geocoded in this X, Y manner failed to match any of the GIS features successfully (this is the 0 score). 479 CFS records matched just the PA address information. 22,392 CFS records matched just the GIS street information. 34,143 CFS records matched either the address or both the parcel and street GIS information. 53,813 CFS records matched both the GIS address information and Property Appraiser address information. 3,011 matched both the GIS street and address point information. Finally 1,193 CFS records geocoded using the X, Y positional information matched all three GIS datasets (parcel, street, and address point information).

Fig-17 summarizes the X, Y geocoder score information and shows the score descriptions. Note that CFS that scored a 3 have two possible explanations. The Max_TotalScore sum for this geocoded result is 385,159. If the max score is 6 then the Max_TotalScore is out of a possible 706,584 (117,763 Unique CFS x 6) giving the X, Y geocoded result a 54.51% (rounded to %55) Total Score percentage. Again the X, Y geocoder had an impressive 97.68% match rate.

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Incident Address Geocoded

Geocoding using the Incident Address Information and Lake County GIS’s geocoder took significantly longer to do. Geocoding against a dataset that has over 100,000 records means that the computer must first store each CFS address then look through the massive lookup lists that make up the County’s Geocoder. Unfortunately using a geocoder to assign a positional location to a text value means that if the geocoder does

not match, the CFS record is not assigned a location. This also writes blank spaces to the blank fields (the records that fail to join).

The formulas for both the Address score and Street score had to be slightly modified to account for these blank spaces, because the in compare method is being used to compare the two fields. If blank spaces are used, the function assigns a score of 3 (address point score function) or 2 (street score function) inaccurately. The newly modified street scoring function is shown in Fig-18. At the end of each IF statement the “and len(BaseStreet) > 2” was added. This will only assign a score of 2 to the street score field if the field is greater than 2 fields. This accounts for the two/one blank space records that may be in these two fields.

The modifications to the address score function are shown in Fig-19. Again the len() method was used to ensure the blank records did not get assigned a score of 3 when the function looking for the AddPtAddress (Address Point Address) value in the I_Fadd (Incident Address) field. The reason this was not needed in the X, Y geocoded result is that these fields had None assigned to these fields during the geocoding (X, Y

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Fig-18 (Barnett, 2015)

Fig-19 (Barnett, 2015)

mapping) process. When spatial joined, these fields still had None assigned to them, which acts as a blank cell vs a space (“ “) value.

None is a different field value than “Null”, and it is shown in Fig-20. The geocoded results that failed to geocode were assigned “Null” values. This acts as a blank space when spatial joined. These small changes ensure that the comparison stays accurate and doesn’t skew the scoring data in favor of one geocoding process. If these changes were not made, the data would have heavily favored the geocoded results vs. the X, Y geocoded results.

The Geocoded results for the County GIS geocoder are shown below, in Fig-21. The large number of “failed” points comes from CFS that failed to geocode. The

“incident address” was used to geocode these points. The incident address is populated by the CFS call taker and doesn’t “have” to match a known address. If the caller is giving bad address information or vague information the call taker may not have much information to go on. This would lead to inaccuracte information in this field, which would cause the geocoder to produce poor locational information.

The match rate for the GIS geocoder is 65.12%. Therefore 65 out of every 100 CFS will geocode instantanously using this method. The geocoder had a 40% total

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Fig-20 (Barnett, 2015)

Fig-21 (Barnett, 2015)

score percetange. While these numbers may seem low, they are fairly good. Another thing to consider is that the sensitivity can be adjusted to get more matches. This analysis used a 50% minimum geocoding score, because this is the percentage normally used in many applications. A minimum match score that is too low can cause the geocoding service to perform poorly because the input data will match more records. This will cause the result dataset returned for each search to be extremely large, which requires more processing power on the hardware hosting that geocoding service. Match sensitivity must be carefully balanced between performance and usability.

Geocoder Conclusion

Evaluating geocoders in a non-traditional manner means making assumptions and this analysis is no different. The functions used to score the corresponding spatial relations are not all encompassing, and could potentially score values incorrectly if a particular field value was not accounted for. All of the functions are based on the address information, parcel information, and street information. All of these fields and their corresponding values are populated by individuals, and individuals make mistakes when creating and editing data. The Incident Address geocoded using the County’s composite geocoding service results in a sub-par match rate.

Summarizing the geocoding results means comparing the match rate for each geocoded dataset, and the total score percentage. The match rate represents the number of CFS that successfully matched a known address. These known address values were generated using Lake County’s Address Point, Parcel, and Street datasets. The dataset that has the best results will be used in the Hot-Spot Analysis. The total score percentage is the “quality” of the match rate. A high total score percentage means that the CFS dataset matched more than just one GIS input dataset. If a CFS matches more than one GIS feature, then the likely hood of that CFS literally occurring in the address listed is much higher

The X, Y values attached to the CFS database clearly have highly accurate positional information. Fig-22, shown below, shows that the X, Y geocoding method had a match rate of 97.68% compared to the 65.12% match rate geocoding the Incident Address.

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Fig-22 (Barnett, 2015)

The X, Y mapped values will be used in the Hot-Spot Analysis, because both the match rate and total score percentage are higher. This indicates that the positional information assigned to each CFS record, in the form of the X, Y values, is highly accurate. The X, Y CFS will provide a very good input layer to be used in the Optimized Hot-Spot Analysis, because of its high degree of positional accuracy.

Reviewing where the CFS that “failed” to join to any GIS features can help quality check the GIS data. If geocoding failure is occurring in the same areas over and over, then it is possible that the GIS input data for these may have inaccurate information. All GIS data in this analysis is, for the most part, is prone to human error. Hundreds of edits are done on these feature classes each week, data is being replicated and reproduced in a variety of manners. It is impossible to have a dataset that is 100% accurate all the time.

Fig-23, on the left, shows the X, Y Geocoded dataset. Red dots indicate a CFS failure to spatially relate, while green dots indicate a CFS that successfully spatially related to at least one GIS input feature. There are significantly less failures for the X, Y Geocoded dataset, but these failures still seem to be localized into three “clusters” or zones. These three clusters also seem to be occurring within city limits. City and County data

can vary in terms of the accuracy of the data. Several cities in Lake County do their own addressing. If the cities fail to report updated / correct information then the County data will be inaccurate or out of date. The 2,000+ CFS failures cluster in the following three areas:

1. Clermont / Minneola Area2. Fruitland Park / Leesburg Area3. Tavares / Eustis / Mount Dora AreaQuantifying these failures may shed light about what areas may need to be reviewed

for accuracy or where the Municipal data may be inaccurate due to communication breakdown between the municipality and the County GIS department. Fig-24, on the next page, shows how many unsuccessful CFS, using the X, Y geocoding method, occurred within each City Limit.

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Fig-23 (Barnett, 2015)

A high number of CFS failed to spatially relate to any GIS datasets (and their corresponding address information) within the Cities of: Lady Lake, Tavares, Clermont, and Leesburg. This indicates that these cities, which all do their own addressing, need to re-evaluate their workflows and communication methods when addressing as it concerns E911 data.

The Incident Address Geocoded dataset (Also known as GIS Geocoder) had approximately 20x more failures when compared to the X, Y geocoded dataset. This is due to the different geocoding method used. The X, Y geocoding process used X, Y values (positional information) already attached to the entire dataset.

The X, Y values for the Incident Address / GIS Geocoded dataset had to be generated through a process known as “geocoding”. This process, outlined in detail in the “Geocoding” section, relies on text matching, which can be error prone. It also relies on an accurate GIS input dataset. The 41,000+ failures can be attributed to three things: First the GIS data, used as the “match” value in the geocoding process, can be inaccurate within municipal areas. Second, the municipal data, reported from the city to the County, is used to populate GIS data within municipal areas. Finally if the data is incorrect or if they data is never reported then no “match” value will be in the geocoder and thus the CFS will not be assigned positional information.

The Incident Address values, used in the geocoding process, are manually entered and can differ in spelling when compared to the GIS dataset. For example: “315 W Main St vs. 315 West Main Street”. The sensitivity of the geocoder determines if such difference will “match” or not match. If a match does occur then X, Y positional information is assigned to that CFS and the CFS is now “spatially” related.

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Fig-24 (Barnett, 2015)

Fig-25, right, shows the Incident Address Geocoded CFS results. These locations were generated by the Incident Address matching to a known address in the GIS Composite Geocoder. Again red dots indicate CFS that failed to spatially relate to any input GIS data. For this geocoded result CFS that fail to geocode, relate to a known text value in the GIS Geocoder lookup tables, are not shown in Fig-25. They are represented and accounted for in table view; but since there is no positional information attached to the CFS record they are not drawn on the map. There is some similarity between the areas of the X, Y failures and the Incident Address failures. This again points to GIS input data having inaccurate information.

If the input data of both geocoders is failing to match address information in specific areas, then the underlying GIS information for these areas should be reviewed for accuracy. This is especially true if these “failure areas”, or areas where CFS are not matching input GIS data (and corresponding address information), are within municipal boundaries. The Incident Address CFS that failed to spatially relate to any input GIS feature are summarized in Fig-26, below.

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Fig-25 (Barnett, 2015)

Fig-26 (Barnett, 2015)

The CFS that “failed to geocode” are not displayed on the map, but are accounted for in the tabular view of the data. Again Tavares, Leesburg, and Lady Lake top the list. These cities also handle their own addressing and assign their own addresses to new development. These cities, and the representative GIS data for them, should be quality checked based on the findings of this analysis.

HOT-SPOT RESULTS

The Hot-Spot analysis used for this project is known as ESRI’s “Optimized Hot-Spot Analysis”. The tool is intended to “identify statistically significant spatial clusters of high values (Hot-Spots) and low values (Cold-Spots). This tool automatically aggregates the incident data. The incident data or input data going into the tool will be the X, Y geocoded CFS dataset. Only the CFS that successfully spatially related to a County GIS feature will be used. This means the 2,731 that failed to relate will be excluded from this analysis. This will further ensure that the CFS being analyzed by the “Optimized Hot-Spot Tool” are valid CFS that are tied to the correct location on the planet.

The Hot-Spot analysis will be performed using the entire CFS dataset (all years), and then broken up by year. A total of 5 Hot-Spot results will be generated for this analysis. Mapping the entire CFS data shows which areas, over time, have the most CFS. 1 Hot-Spot output for the entire CFS dataset, and 1 Hot-Spot output per year (4 years). The resulting Hot-Spot will represent areas that presently consume more EMS resources every year. Mapping Hot-Spots per year will show if the development patterns, over time, have shifted the Hot-Spot areas. As new development occurs, the areas that consume EMS resources may change.

The method for creating Hot-Spot is based on the “weighted points” concept. This concept accounts for small locational variances and aggregates the CFS using weighted points. The weighted points are generated in a process that aggregates localized CFS together. The number of weight points generated is based on the amount of input CFS records used in the analysis. These points are then “weighted” (scored) compared to each other to get an average for the region. This regional average is then compared to individual weighted points. If the weighted point average is statistically significant (+/- 1 to 3 standard deviations away from the mean) then it represents a Hot-Spot or Cold-Spot. Each Hot-Spot output will be listed along with its:

Number of Outliers Number of Weighted Points generated by the Aggregation Process Aggregation Distance Optimal Fixed Distance Band Number of statistically significant output features based on FDR correction

This will help show how the Hot-Spot parameters change from year to year based on the input CFS data. It will also provide an understanding of what parameters the tool used to generate the resulting output. If a Hot-Spot output differs significantly, these values can be used to identify the different parameters used in generating that output.

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Hot-Spots Outputs

The first Hot-Spot generated is for the entire CFS dataset (2012 – 2015), and it is shown in Fig-27, on the next page. The Hot-Spots by year are shown after that in ascending order. The parameters for each result are shown in the table above each output. Figures 28 – 31 show the Hot-Spot outputs for each year. The 2012 CFS dataset has a limited number of records because it starts on 10/1/2012 and ends on 12/30/2012. 2013 and 2014 CFS datasets represents a full year (excluding 12/31). Finally the 2015 CFS dataset ranges from 1/1/2015 – 10/1/2015. The parameters change in relation to the dataset used. None of the Hot-Spot outputs actually had “Cold-Spots”, which would represent areas that have significantly lower rates of CFS. Therefore all of the outputs show only “Hot-Spots” or areas where CFS activity is significantly higher, compared to the rest of Lake County.

The 2012 CFS dataset, shown in Fig-28, has both the highest Optimal Fixed Distance Band and Aggregation Distance values, because it uses the least number of CFS records (compared to the other CFS yearly datasets). The tool must accommodate for the lack of records by increasing the distance that aggregates and clusters CFS records together. The 2012 CFS dataset shows large Hot-Spot areas in Clermont, Tavares / Eustis area, and Leesburg. The 2013 CFS dataset, shown in Fig-29, shows several new Hot-Spots forming in Lake County. The first Hot-Spot is located in the South Eastern region of the County. The second is located near the Northern tip of the municipal boundaries of Umatilla. The 2014 CFS dataset, shown in Fig-30, shows all of the above Hot-Spot areas growing in the number of Hot-Spots around the region. It also shows a new Hot-Spot in the City of Groveland. The 2015 CFS dataset, shown in Fig-31, again shows all of the above Hot-Spot areas growing in the size and number, but the Hot-Spot in Groveland has shifted to the Southern region of Groveland.

Showing the CFS Hot-Spots per year shows how time can affect the Hot-Spot location. New development occurs all the time in Lake County. New development changes the landscape of CFS by bringing new residents into the County and shuffling the existing residents’ location. The CFS data must be up-to-date to capture this shift. Development in Lake County has known to be picking up in the Clermont and Lady Lake areas. An extension of the prominent “Villages Retirement Community” is being finalized. This community is for residents over 55 years old, which is the age bracket known to consume the most EMS resources.

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CFS 2012 – 2015 Summary 818 Outliers 34,067 Aggregated PointsAggregation Distance: 11.13 Meters Optimal Fixed Distance Band: 870 Meters3,400 Statistically Significant Features Generated

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Fig-27 (Barnett, 2015)

CFS 2012 Summary114 Outliers 3,812 Aggregated “Weighted” Points GeneratedAggregation Distance: 59 Meters Optimal Fixed Distance Band: 1,618 Meters579 Statistically Significant Features Generated

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Fig-28 (Barnett, 2015)

CFS 2013 Summary350 Outliers 14,034 Aggregated “Weighted” Points GeneratedAggregation Distance: 22.18 Meters

Optimal Fixed Distance Band: 930.86 Meters

1,524 Statistically Significant Features Generated

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Fig-29 (Barnett, 2015)

CFS 2014 Summary343 Outliers 11,638 Aggregated “Weighted” Points GeneratedAggregation Distance: 28.2 Meters

Optimal Fixed Distance Band: 851.02 Meters

1,153 Statistically Significant Features Generated

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Fig-30 (Barnett, 2015)

CFS 2015 Summary281 Outliers 10,345 Aggregated “Weighted” Points GeneratedAggregation Distance: 26.7 Meters

Optimal Fixed Distance Band: 1,044.78 Meters

1,224 Statistically Significant Features Generated

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Fig-31 (Barnett, 2015)

CONCLUSION & RECOMMENDATION

Who Calls evaluated the geocoding method, and concluded that the X, Y values attached to each CFS record have a high degree of positional accuracy. With a match rate of almost 98%, the X, Y values, which will be used as the locational information for the Hot-Spot analysis, showed a surprising degree of accuracy. During the geocoding evaluation process it was noted, however, that CFS located within several areas “failed” to match either geocoded dataset (The X, Y or Incident Address).

These areas corresponded to the municipal boundaries of Leesburg, Tavares, Clermont, and Lady Lake. This could be attributed to the fact that these areas, on average, experience more CFS than other areas. As such, the number of CFS that fail to geocode will increase solely because there are more CFS originating here. Either way these areas should be quality checked to determine if the GIS information for the corresponding city features have accurate address information attached to them.

The Hot-Spot analysis results show that Hot-Spots are occurring within or along municipal boundary lines. This further reinforces the need to have accurate data for municipal areas. Municipal areas experiencing significantly more CFS need to ensure that the County data is accurate for their boundaries, because the County data is directly consumed in the E911 process. If the data for these areas is out-of-date or invalid then CFS coming from these areas may experience slower response times.

Locating where an emergency is occurring is only half the battle of EMS. The other half, which is not covered in this analysis, is actually dispatching resources to the location of a CFS. This process also utilizes Lake County’s GIS data, again highlighting the need for accurate data in these areas. Reporting new development and updating existing development within these areas falls largely on the municipality. If the municipality is responsible for its own addressing and street construction, the need for accurate reporting is even more critical. County GIS information can only be updated if reported. If the data is not updated, the inaccurate information is used in the dispatch process and response times can increase.

The Hot-Spot analysis shows that each year the number of Hot-Spots grows. Each year the majority of Hot-Spots occurred within municipal boundaries. In the 2015 Hot-Spot result, the Hot-Spots are shown occurring just outside several municipal boundaries. The need for ISBA agreements with these cities will allow the jurisdictional boundary lines to be ignored when responding to these areas. That is important when the areas that are experiencing significantly more CFS, compared to the County as a whole, are located within or near municipal boundaries. Trying to identify if the CFS falls in the City or County jurisdiction can add to the response time of a CFS, which is why ISBA were created.

In conclusion Who Calls shows that CFS in Lake County are occurring in areas where the data quality seems to be lacking. Each year the number of CFS within municipal boundaries grows. This indicates the critical need for accurate information within these municipal boundaries in order to respond to CFS quickly. The responsibility of the data quality falls largely on the municipality, while identification of problem areas falls largely on the County.

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Known Project Limitations

Who Calls uses a weighted scoring system that is very objective, which used to populate the “Total Score %”, and is used in some was as a measure of geocoding accuracy. A better method may be to use a 1 vs. 0 scoring system when evaluating the reference data to the Incident Address information in the CFS. CFS that successfully relate to the reference data would be given a score of 1 in this manner as opposed to the “1, 2, 3” scoring system used in this project. Then features that successfully relate to 2 reference datasets are “twice” as accurate as CFS that relate to only one of the reference inputs. Features that relate to all of the reference datasets would be given a “3” and thus be three times as accurate. The total number of points would be 3 times the number of CFS being analyzed instead of 6, thus reducing the amount of points and increase the Total Score % for each geocoded output.

Automating Future Analysis

The Hot-Spot methodology outlined above can be used to generate future Hot-Spot outputs. A model can be generated to answer planning questions that could filter for specific CFS data including: specific call times, specific types of call, and calls with specific response times.

Administrators could “ask” questions by changing the filter. This could lead to a better understanding of the CFS and better quantify the need for certain areas. Administrators could sort based on the “Call Type” looking for CFS that could be focused on finding patterns based on time periods. Are CFS occurring in different volumes during the PM vs. AM hours? Are CFS occurring in different volumes in different areas based on different seasons? CFS could also be reviewed for things like Fire vs Medical CFS or Municipal vs. County CFS.

For this model to be able to account for such variables, a process needs to be created, and it needs to orientate the CFS data in a manner that accounts for date, time, and location, as this analysis does. The CFS data must have a schema that is standardized and integrated in a way that is both easily extracted from the LEMS server and easily integrated into GIS format. Then the CFS can be georeferenced using whatever geocoding service makes the most sense, and then can undergo spatial analysis based on the findings above.

The CFS will then be aggregated, analyzed for Hot-Spots, and outputs that are accurate can quickly be generated. This allows the “research” part for administrators to be streamlined, and allows the data to be easily filtered with no special training. The model can be published online and ran in a customizable web interface that can be password protected and used whenever the tool is needed.

There are over 66 different “Call Types” recognized by the current 911 system. There are 12 currently recognized Emergency Agencies with 250 units between these agencies. The CFS dataset is robust and the amount of analysis that could take place looking for patterns based on these field values is immense. Providing this ability inside of an easy to use non-technical environment could empower Emergency Service Providers with the ability to make decisions with the data supporting those decisions if the time comes to do so.

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REFERENCES

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https://www.udel.edu/johnmack/frec682/cholera/D'Marko, D. (2015, June 23). No Changes for Lake County Fire Funding. Retrieved from

Mynews13: http://www.mynews13.com/content/news/cfnews13/news/article.html/content/news/articles/cfn/2015/6/23/no_changes_for_fire_.html

ESRI. (2015). How Optimized Hot-Spot Analysis Works. Retrieved from ESRI Toolbox Description ArcMap 10.2: https://desktop.arcgis.com/en/desktop/latest/tools/spatial-statistics-toolbox/how-optimized-hot-spot-analysis-works.htm#GUID-B415BC50-03A9-4E47-BE78-6318A5C871B0

Fischer, M. M., & Nijkamp, P. (2013). Handbook of Regional Science.Goodwin, C. G., Schoby, J., & Council, W. (2014, September). A Hot-Spot Analysis of

Teenage Crashes. SWUTC, 1-33.Lake County. (2015, October 15). Visit Lake FL. Retrieved from Lake County Florida:

http://www.visitlakefl.com/Plan/AboutLakeCountyLake County Fire Rescue. (2015, September 1). Fire Rescue. Retrieved from Lake

County FL: https://www.lakecountyfl.gov/departments/public_safety/fire_rescue/Medical University of South Carolina. (2015, July 22). Healthy Aging. Retrieved from

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8.Molla, M. M., Lee, E., & Stone, M. L. (2014, November). Geostatistical Approoach to

Detect Traffic Accident Hot Spts and Clusters in North Dakota. UGPTI, 1-23. Retrieved 11 1, 2015, from http://www.ugpti.org/pubs/pdf/DP276.pdf

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Roongpiboonsopit, D., & Karimi, H. A. (2010). Comparative evaluation and analysis of online geocoding services. International Journal of Geographical Information Science, 1081-1100. Retrieved from http://dx.doi.org/10.1080/13658810903289478

Stanford, L. (2014, October 31). Fire officials hopeful ISO rating will improve. Retrieved from Daily Commercial: http://www.dailycommercial.com/news/article_6c865979-826c-50e59f1f-9cae46a4c798.html

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Stanford, L. (2014, December 28). Yatesville's ISO rating declines. Retrieved from Tribune Business News: http://search.proquest.com/docview/1640731398?accountid=10920

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Figures Used

Fig-1. (Lake County, 2015)

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Fig-2. (Well Florida Council, 2015)

Fig-3. Moran’s I (ESRI, 2015)

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Fig-4. (ESRI, 2015)

Fig-5. (Cheffins & Lith, 2015)

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Fig-6. (Barnett, 2015)

Fig-7. (Barnett, 2015)

Fig-8 (Barnett, 2015)

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Fig-9 (Barnett, 2015)

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Fig-18 (Barnett, 2015)

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Fig-19 (Barnett, 2015)

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Fig-21 (Barnett, 2015)

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Fig-22 (Barnett, 2015)

Fig-23 (Barnett, 2015)

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Fig-24 (Barnett, 2015)

Fig-25 (Barnett, 2015)

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Fig-26 (Barnett, 2015)

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Fig-27 (Barnett, 2015)

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Fig-28 (Barnett, 2015)

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Fig-29 (Barnett, 2015)

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Fig-30 (Barnett, 2015)

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Fig-31 (Barnett, 2015)

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Biographic Sketch

Brandon majored in Urban and Regional Planning while simultaneously working as a GIS Administrator for Lake County Board of County Commissioners. He also currently volunteers in various leadership positions for a local Toastmasters club, and he received his master’s degree in the fall of 2015. Brandon’s creativity and passion for GIS and technology has led to the creation of several web GIS applications and presentations including an appearance at Central Florida GIS.

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