jingru zhang portfolio 2015new

LEVY

MARION

CITRUS

SUMTER

Wildwood

Chiefland�

Legend

Final Turnpike ExtensionStudy Area

Chiefland�

Wildwood

Halfway PointI-75 HighwayMajor RoadsSurface WaterAgricultual ParcelsConservaton Ar eas�FloodZone 0 12,000 24,000 36,000 48,000

6,000

MetersHaving contracted with Florida Department of Transportation, we determined

the location of the planned expansion of roadways and the site of the accompanying service plaza. In spite of following the requirements of FDOT,

we captured the public concerns. Hence the factors that should be enrolled into

consideration consists of avoiding the surface water, wetlands ,flood zones

and conservation areas; areas with soils that do not readily support road and

building construction and steep slopes should be excluded from the proposal.

In addition, we will not involved any urban land uses into the construction

of the service plaza. The process of path finding and site selection are based on the professional advices from FDOT.

Recommended Service Plaza

Analysis for Turnpike Extension - Located from the current northern terminus west of Wildwood to the intersection with US 19

just south of Chiefland in Levy County- A good axis which creates a set of linkage with surrounding major roads.

- Avoiding water bodies, wetlands, conservation areas and flood zones.

- Passing through flat areas with soils that are mostly well-drained.

Legend

0 - .458811921

.458811921 - 1.261732782

1.261732783 - 2.064653643

2.064653644 - 2.982277485

2.982277486 - 4.014604307

4.014604308 - 5.391040069

5.39104007 - 7.799802653

7.799802654 - 12.61732782

12.61732783 - 29.24925995

Legend

Cost Raster

Value

High : 17.35

Low : 2

Legend

Final Service Plaza

Final Turnpike Extension

Final Sites Raster

<VALUE>

1.850000024 - 3.224313745

3.224313746 - 3.991372567

3.991372568 - 4.790392172

4.790392173 - 5.429607857

5.429607858 - 5.972941188

5.972941189 - 6.676078441

6.676078442 - 7.475098047

7.475098048 - 8.242156868

8.242156869 - 9.5

9.500000001 - 10

Agricultual Parcels

Service Plaza

Type of land use: TimberlandPrice: 453793 per acreArea: 32.07 acres

Analysis for Service PlazaThe proposed service plaza follows the basic requirements both from FDOT and public views.

-�Adjacent to the proposed turnpike extension, especially near the halfway point

-�Located on soils with proper drainage conditions-�Avoid flood zone and steep areas-�Maintaining a proper distance to urban land uses

0 460 920 1,380 1,840230

Meters

Legend

Final Turnpike Extension

County Boudary

Study Area

4 Counties

URP 6270 SECTION 5421 PROJECT 2 JINGRU ZHANG

0 72,000 144,000 216,000 288,000

36,000

Meters

Alternative2

Alternative1

Recommended

Solar Energy Reaesrch Park And Academy

LEGENDFinalSelection_Project

par_within500>25_final

Main_CONTROL

Parcel_Quiet_Selection

soil_Drainwell_selection

<all other values>

DRAINAGECL

EXCESSIVELY DRAINED

MODERATELY WELL DRAINED

WELL DRAINED

majrds_feb11

0

3,200

6,400

1,600

Meters

1 in = 2 miles

Alternative2

Alternative1

Recommended

LEGENDFinalSelection_Project

Parcel_Quiet_Selection

soil_Drainwell_selection

<all other values>

DRAINAGECL

EXCESSIVELY DRAINED

MODERATELY WELL DRAINED

WELL DRAINED

majrds_feb11

Potential locatio

n of the sit

es for th

e solar e

nergy

research

park and academy should be based on th

e

following cri

teria:

� - Located with

in 2.5 miles o

f the cit

y limits

of Masco

tte, FL

� - Must n

ot be with

in 3000 feet o

f any water f

eatures

� - Not w

ithin 1 m

ile of a school.

� - Not w

ithin 1 m

ile of any airp

ort.

� - Not w

ithin any co

nservatio

n lands.

� - Not w

ithin 1000 feet o

f a sinkhole.

� - Located on so

ils with

proper drainage.

� - Located with

in 500 feet o

f an existin

g major ro

ad .

� - Site m

ust be a m

inimum of 25 acre

s.

� - Proposed to

locate within agricu

ltutra

l landuse area.

- 25 acers i

n total

- Exce

ssively drained ground

- Price

: $24901.57 which

is relative

ly economic site

for in

vestment.

- Ideal dista

nce away from re

sidential places

- Right a

longside th

e mainroad

- Abundant sp

aces for fu

rther e

xpansion

- 25 acers i

n total

- Price

:$40257.33,which is

money-co

st

compaired with

the re

commended one.

- Very exce

ssively drained sy

stem.

- Located on th

e junction of tr

ansportatio

n

- Resid

ential nearby hinders expansio

n

- As it

is located on th

e boundary of the

chosen o

verall contro

lling area , it

might

activate th

e perip

hery high-tech development.

Text

- 25 acers i

n total .

- Price

: $32738.88.However it is

surrounded by

unavailable area so th

at outward expansio

n is

very lim

ited.

- Exce

ssively drained sy

stem.

- Located on th

e junction of tr

ansportatio

n

- Resid

ential nearby hinders expansio

nURP6270 Section5421 Project 1

Jingru ZHANG

0

450

900

225

Meters

´

JINGRU ZHAHG | URBAN & REGIONAL PLANNING

(352)283-2658 • [email protected]

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Graduate Thesis: A Parcel-Level Analysis of Coastal Hazard Impact on Manatee County’s Residential Lands: An Integration of GIS, HAZUS-MH and Land Use

Contact Info

GeoDesign: Spatial Implications of Sea-Level-Rise Policies on Future Development Patterns

Northeastern Florida Conservation Land Use Suitability Modeling • Lands Suitable for Maintenance of Ecological Process and Service

Using Census Longitudinal Employer-Household Dynamic Data to Assess RTS Transit Service Coverage

Estimate the impact of Miami-Dade Metro Rail System on Land Just Value

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3

JINGRU ZHAHG (352)[email protected] SW 39th Blvd aPt 432gaineSville, fl, 32608

Ma, urBan and regional PlanninguniverSity of florida

BS,urBan and rural Planning & reSource ManageMent

huaqiao univerSity

Source: Esri, DigitalGlobe, GeoEye, i-cubed, Earthstar Geographics, CNES/Airbus DS, USDA, USGS,AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community

¯0 1 2 3 40.5

Miles

My study estimated buildings and building stock replacement value

exposed to projected coastal hazard scenarios. Residential properties are most vulnerable to the attacks of coastal hazards. Hence, my study summarized future residential land exposures and associated existing land uses by scenarios. Land use exposures were categorized by spatial locations with respect to the magnitude of sea level rises and associated storm surges. My study also developed a methodology to present the projected storm water depth at the parcel

level, and to identify the number of properties in each parcel. For categorized future residential capacities, my study supported the community developments while also reduced their vulnerability to coastal hazards. Suggestions were made in views of county’s land use plan, coastal elements, construction strategies and flood insurance.

HAZUS-MH was used to create 3 scenarios in terms of the conditions about sea-level-rises and associated storm surges. The techincal process included

delineating coastal floodplain, shoreline characterization with default dataset, producing storm water grids, and to produce the Hazard Event Report. The Event Report estimated the number of buildings in the study region, which havs an aggregated total replacement value and are also exposed to the storm surge. Additionally, the reports presented the distribution of the value with respect to the general land-use types by Study Region and Scenario respectively.

Parcels with storm water depths; darker colors represent higher average depths. Manatee County, FL 2040

RESEARCH FRAMWORK & INTRODUCTION OF HAZUS-MH

WILL cOAstAL fLOOd hAzARds AffEct thE cOUNty’s LANd dEvELOPmENt? WhAt stRAtEGIEs cAN hELP ImPROvE thE INtEGRAtION Of LANd UsE PLANNING ANd cOAstAL hAzARd AdAPtAtION?

4

Source: Esri, DigitalGlobe, GeoEye, i-cubed, Earthstar Geographics, CNES/Airbus DS, USDA, USGS,AEX, Getmapping, Aerogrid, IGN, IGP, swisstopo, and the GIS User Community

¯0 1 2 3 40.5

Miles“condominium improved,” is a land use category which has stacked parcels on exactly the same location. In this case, each parcel not only consists of multiple individual units, but also contains more than one census block. With the GIS-produced statistics of coincident events, we would be able to check parcels individually and to know the floods' impact based on the number of registered properties and the occupancy structures, as well as the local storm wave heights

arcGIS was proactively used in compiling and disaggregating the land use information into the parcel dataset. More importantly , it was also used for producing Zonal Statistics of the storm water depth. The zonal stats were then intersected with the parcels, in order to explore future residential lands and their existing condition, the special concerns about "stacked properties", as well as the vacant future capacities.

Parcels with storm water depth: a stillwater-elevation-based storm surge

An inundated Condo Complex with 212 Registered Properties within 187 building footprints

An inundated Condo Complex with 322 Registered Properties within 63 building footprints

An inundated Condo Complex with 46 Registered Properties and 23 building footprints

Parcels with storm water depth: a stillwater-elevation-based storm surge, with the average depth ≥ 4 feet

Residential parcels with storm water depth: a stillwater-elevation-based storm surge, with the average depth ≥ 4 feet

GIS ANALYSIS WITH STORM SURGE GRIDS PRODUCED BY HAZUS

5

GRAdUAtE thEsIs: A PARcEL-LEvEL ANALysIs Of cOAstAL hAzARd ImPAct ON mANAtEE cOUNty’s REsIdENtIAL LANds: AN INtEGRAtION Of GIs, hAzUs-mh ANd LANd UsE

County Boundary

Census Blocks

Vacant Lands inside a 100-r Stillwater SS and Inside Potential SLR ZonesSLRPCT40, LUC_DESCRI

1, Vacant Res. Common Area (1554)(New 2014),Vacant Res.Common Area (1554)(New 2014)

1, Vacant Residential w/Site Amen. (1554),Vacant Residential w/Site Amen (1554)

1, Vacant Mobile Home Lot Platted (1554)

1, Vacant Res.Common Area (1554)(New 2014),Vacant Res. Common Area (1554)(New 2014)

1, Vacant Condominia Residential (1554)

1, Vacant Non-Residential/Unusable (1555)

1, Vacant Residential Tract/Unusable (1554)

1, Vacant Residential Platted (1554)

1, Vacant Commercial (1555)

slr_1m

still_proValue

High : 18.2834

Low : 8.58307e-006

¯0 1.5 3 4.5 60.75

Miles

County Boundary

Census Blocks

Vacant Lands inside a 100-yr Stillwater SS but OUT of Potential SLR ZoneSLRPCT40, LUC_DESCRI

999, Vacant Mobile Home Lot Platted (1554)

999, Vacant Res.Common Area (1554)(New 2014),Vacant Res. Common Area (1554)(New 2014)

999, Vacant Acreage,Not Ag. 10+ Acres (1555),Vacant Acreage,Not Ag.10+ Acres(1555)

999, Vacant Institutional (1555)

999, Vacant Commercial w/Impv (1555)

999, Vacant Residential Platted (1554)

999, Vacant Residential Tract/Unusable (1554)

999, Vacant Residential w/Site Amen. (1554),Vacant Residential w/Site Amen (1554)

999, Vacant Condominia Residential (1554)

999, Vacant Res. Common Area (1554)(New 2014),Vacant Res.Common Area (1554)(New 2014)

slr_1m

still_proValue

High : 18.2834

Low : 8.58307e-006

¯0 1.5 3 4.5 60.75

Miles

County Boundary

Census Blocks

Vacant Lands inside a Half-Meter SLR Induced SS Surface but Out of Stillwater<all other values>

LUC_DESCRIVacant Condominia Residential (1554)

Vacant Res. Common Area (1554)(New 2014),Vacant Res.Common Area (1554)(New 2014)

Vacant Res.Common Area (1554)(New 2014),Vacant Res. Common Area (1554)(New 2014)

Vacant Residential Platted (1554)

Vacant Residential Tract/Unusable (1554)

Vacant Residential w/Site Amen. (1554),Vacant Residential w/Site Amen (1554)

half_proValue

High : 19.7381

Low : 1.43051e-005

¯0 1.5 3 4.5 60.75

Miles

Vacant Lands Exposure-Potential Future Residential Uses

County BoundaryCensus Blocks<all other values>

LUC_DESCRIVacant Condominia Residential (1554)Vacant Res. Common Area (1554)(New 2014),Vacant Res.Common Area (1554)(New 2014)Vacant Res.Common Area (1554)(New 2014),Vacant Res. Common Area (1554)(New 2014)Vacant Residential Platted (1554)Vacant Residential Tract/Unusable (1554)Vacant Residential w/Site Amen. (1554),Vacant Residential w/Site Amen (1554)

full_proValue

High : 22.2779

Low : 2.19345e-005

¯0 1.5 3 4.5 60.75

Miles


Class 4 - FullSSVacOther Vacant LandsVacant Residential

Class 3 - HalfSSVacOther Vacant LandsVacant Residential Vacant Residential

Class 2 - StillOutSLRVacSLRPCT40, LUC_DESCRI

Vacant ResidentialOther Vacant Lands

Class1 - StillInSLRVacSLRPCT40, LUC_DESCRI

Vacant ResidentialOther Vacant LandsCounty BoundaryCensus Blocks

full_proValue

High : 22.2779

Low : 2.19345e-005 ¯ 0 1.5 3 4.5 60.75Miles








full_proValue

High : 22.2779

Low : 2.19345e-005 ¯ 0 1.5 3 4.5 60.75Miles

Vacant lands as residential future capacities: inside a 100-yr stillwater-based storm surge

area and will not be affected by a potential 1m sea-level-rise

Vacant lands as future capacities: inside a 100-yr stillwater-based storm surge area , the storm surges are induced by a 0.5m SLR

Vacant lands as future capacities: inside a 100-yr

stillwater-based storm surge area , the storm surges are

induced by an 1m SLR

Vacant lands as future residential capacities: inside a 100-yr stillwater-based storm surge area and might be affected by a potential 1m sea-level-rise

6








full_proValue

High : 22.2779

Low : 2.19345e-005 ¯ 0 1.5 3 4.5 60.75Miles








full_proValue

High : 22.2779

Low : 2.19345e-005 ¯ 0 1.5 3 4.5 60.75Miles








full_proValue

High : 22.2779

Low : 2.19345e-005 ¯ 0 1.5 3 4.5 60.75Miles

CATOGRIZATIONS OF CURRENTLY vACANT FUTURE RESIDENTIAL

7

GRAdUAtE thEsIs: A PARcEL-LEvEL ANALysIs Of cOAstAL hAzARd ImPAct ON mANAtEE cOUNty’s REsIdENtIAL LANds: AN INtEGRAtION Of GIs, hAzUs-mh ANd LANd UsE

hOW mUch Of thE BUILt ENvIRONmENt, If dEvELOPEd At thE cURRENt tRENd, WOULd BE POtENtIALLy INUNdAtEd WhEN sEA LEvEL RIsEs? hOW WOULd thE cOUNty’s LANd UsE PAttERNs chANGE cOmPARAtIvELy If POLIcIEs WERE PUt IN PLAcE tO LImIt URBAN dEvELOPmENt IN cOAstAL hAzARd AREAs?

This study explores the impacts on projected spatial development patterns based on restrictive land development policies, with the goals of mitigating potential economic loss resulting from the increased vulnerability of the coastline based on 1meter of sea level rise. GIS was used along with population projections and current land use data to analyze growth and potential development at a regional scale.

We modeled a projection of Hillsborough County’s current trend of land- use patterns into 2045, and analyzed how much of the new development would fall within future storm surge projections. An alternative land-use scenario was created to discourage new development in high risk areas.

In ArcMap, we used per-acre densities from the County's future land-use plan to visualize the proportional allotment of development occurring on parcels suitable for redevelopment, infill areas, as well as parcels with conservation priorities.

THE GEODESIGN PROCESS

GEOdEsIGN mEthOd: LANd-UsE cONfLIct IdENtIfIcAtION stRAtEGy (LUcIsPLUs). "A PROcEss Of LANd UsE ANALysIs ANd POPULAtION ALLOcAtIONs UsING tRAdItIONAL sUItABILIty tO IdENtIfy cONfLIcts."

3D visulization of Tampa Bay area and central business district.

ArcScene and ArcGlobe

8

Comparing residential allocations. The trend scenario: 37,775; SLR

scenario: 30,280 (Acres)

Comparing Residential Allocations. Trend scenario: 55,183; SLR Scenario: 33,052 (Acres)

Retrieving from coastal hazard areas: combined residential and employment allocation.

The trend of land development: combined

residential and employment allocation

EMPLOYMENT DENSITY , HILLSBOROUGH COUNTY 2050

9

GEOdEsIGN: sPAtIAL ImPLIcAtIONs Of sEA-LEvEL-RIsE POLIcIEs ON fUtURE dEvELOPmENt PAttERNs

Objective 6.1: Lands proximal to hazadous waste sites

Objective 6.2: Lands significant for the process of wildfire movement

Objective 6.3: Lands important for maintaenance of the process of flooding and flood storage

Objective 6.4: Lands proximal to both fire and flooding processes

GOAL 6: IDENTIFY LANDS SUITABLE FOR ECOLOGICAL PROCESS AND SERvICES

10

NortheasterN Florida CoNservatioN laNd Use sUitability ModeliNg • laNds sUitable fOR mAINtENANcE Of EcOLOGIcAL PROcEss ANd sERvIcE

THE GOAL OF CONSERvATION

dEtERmINE thE LANds mOst sUItABLE fOR mAINtAINING EcOLOGIcAL INtEGRIty IN thE 5 cOUNtIEs Of NOtthEAstERN fLORIdA

This project focused on developing decision-making skill at the regional scale using Spatial Analyst, Model Builder,w and land use suitability analysis techniques within a group and individual setting. Our group defined goals and objectives for future land use decisions as they relate to the region's conservation priority. Our goals and objectives were intended to be used to guide a regional GIS analysis to identify future suitability for the conservation category within the study area.

The goal of conservation consists of my Goal 6 and 7 other goals with regard to water quality, landscape integrity, biodiversity, and the economic feasibility of development.

11

¯

Blocks Covered by Service AreasTrasit Supportiveness Status

Both

Home Yes Work No

Neither

Work Yes Home No

City Limit

0 0.8 1.6 2.4 3.20.4Miles

¯

City Limit

Census Block Group Transit Supportiveness

3 - Both Supportive

2 - Job- Based Supportive

1 - Home-Based Supportive

Areas within a 1/4 Mile Network Distace of a Stop

0 0.8 1.6 2.4 3.20.4Miles

"thE PLANNING mEthOd"

"thE ALtERNAtIvE mEthOd"

¯

Census Blocks with LEHD InformationBlocks Serving as Home Places

9999 - Neither Supportive

-3 - Both Supportive

-2 - Job-Based Supportive

-1 Home-Based Supportive

City Limit

0 0.8 1.6 2.4 3.20.4Miles

¯

Census Blocks with LEHD InformationBlocks Serving as Work Places

9999 - Neither Supportive

-3 - Both Supportive

-2 - Job-Based Supportive

-1 - Home-Based Supportive

City Limit

0 0.8 1.6 2.4 3.20.4Miles

The “Home” locations (block groups) covered by RTS's service, with respect to level of transit supportiveness.

RTS's network-based service areas intersected with the transit-supportive block groups

¯

City Limit


3 - Both Supportive



0 0.8 1.6 2.4 3.20.4Miles

¯

City Limit


3 - Both Supportive



Areas within a 1/4 mile Distance from a Stop

0 0.8 1.6 2.4 3.20.4Miles

The “Work” locations (block groups) covered by RTS's service, with respect to level of transit supportiveness.

The transit-supportive census block groups,

city of Gainesville.

The transit-supportive block groups intersected with buffer-based RTS's service areas

Block groups, both transit-supportive and covered by RTS's service as either the "wotk" or "home” location

12

75% 49%

“thE PLANNING mEthOd"BAsEd ON A qUARtER-mILE EUcLIdEAN dIstANcE Of thE tRANsIt stAtION

thE ALtERNAtIvE mEthOdBAsEd ON A qUARtER-mILE NEtWORk dIstANcE Of thE tRANsIt stAtION

of the land area within the RTS’s buffered quarter-mile service areas can be defined as “transit supportive” based on either job or housing density

of the land area within the RTS’s network-based quarter-mile service areas can be defined as “transit supportive” based on either job or housing density

hOW WELL Rts's tRANsIt sERvIcE cOvERs REsIdENtIAL, ANd EmPLOymENt LOcAtIONs ANd hOW WELL It mEEts thE tRAvEL dEmANd BEtWEEN thE PLAcEs WhERE PEOPLE LIvE ANd WORk?

This research is to understand the service quality of the Gainesville Regional Transit System by analyzing the transit service coverage, or the proportion of regions served by RTS transit. "The Planning Method" evaluated RTS's service coverage based on the share of transit-supportive land acreage covered by RTS transit service. The GIS Buffer tool was used to produce the service area. Census blocks were coded with the level of transit supportiveness, based on residential and employment densities.

The alternative method measured RTS's service coverage based on the number of commutes covered by RTS transit service. This method identified that a pair of Census Blocks (the places where people live and work) shares a “job” or a

trip to work and back. ArcGIS Network Analyst was used to produce service areas. The result shows the share of commutes (the job flows) covered by RTS's network service areas

GIS ANALYSIS WITH STORM SURGE GRIDS PRODUCED BY HAZUS

The estimation of the service coverage was conducted in a crossed table. Every commute was identified either as the home location or the work location. And each of the locations has fields showing its transit accessibility (“within “or “not within” the quarter-mile buffer of network service area) and its level of transit supportiveness.

13

UsING cENsUs LONGItUdINAL EmPLOyER-hOUsEhOLd dyNAmIc dAtA tO AssEss Rts tRANsIt sERvIcE cOvERAGE

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!( Sources: Esri, H

ERE, DeLorm

e, USGS, In

termap, in

crement P Corp., N

RCAN,

Esri Japan, M

ETI, Esri C

hina (Hong Kong), E

sri (Thailand), T

omTom,

MapmyIndia, © OpenStre

etMap contributors,

and the GIS User Community

GWR Rail Statio

n Coefficient V

alue

C12_RailDi

!(-0.072023 - -

0.068120

!(-0.068119 - -

0.066363

!(-0.066362 - -

0.065069

!(-0.065068 - -

0.063871

!(-0.063870 - -

0.062282

!(-0.062281 - -

0.057677

!(-0.057676 - -

0.046761

!(-0.046760 - -

0.032140

!(-0.032139 - -

0.028836

!(-0.028835 - -

0.023681

MetroRail

!PRailStation_Pro

0̄

1

2

3

4

0.5

Miles

This research identified the factors that affect the housing value (just value) around the Miami MetroRail area, it sought to determine the spatial relationship between the metro-rail networks and the housing system. With ArcGIS Spatial Statistics extension and Geostatistical tools, mutiple variables were investigated through a l inear regression model (Ordinary Least Square) and Geographically Weighted Regression.

Diagnostic statistics (adjust R-square) reveals that GWR as a local model better fits the dataset than the OLS model (a global model).The GWR results show that the age of building, other than the proximity to metro-rail stations, is the most significant factor that led to the variation of housing just value. Model diagnostic information shows issues of missing key variables. Hence, in order to better justify the model, it is important to further solicit input variables,

!P

!P

!P

!P

!P

!P

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!P!P

!P!P

!P

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!P!P!P!P

!P

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!P

!P

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Sources: Esri, HERE, DeLorme, USGS, Intermap, increment P Corp., NRCAN,Esri Japan, METI, Esri China (Hong Kong), Esri (Thailand), TomTom,MapmyIndia, © OpenStreetMap contributors, and the GIS User Community

OLS Standard ResidualStdResid!( < -1.5 Std. Dev.

!( -1.5 - -0.50 Std. Dev.

!( -0.50 - 0.50 Std. Dev.

!( 0.50 - 1.5 Std. Dev.

!( 1.5 - 2.5 Std. Dev.

!( > 2.5 Std. Dev.

MetroRail

!P RailStation_Pro0̄ 1 2 3 40.5

Miles

The OLS model explains 85.9154% of the variation in the dependent variable.

Residuals are clustered, showing mising variables

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StdResid!( < -1.5 Std. Dev.

!( -1.5 - -0.50 Std. Dev.

!( -0.50 - 0.50 Std. Dev.

!( 0.50 - 1.5 Std. Dev.

!( > 1.5 Std. Dev.

MetroRail

!P RailStation_Pro0̄ 1 2 3 40.5

Miles

The GWR model explains 86.7377% of the

variation in the dependent variable. However, Spatial autocorrelation test shows

model misspecification, which means that there are

important variables missing.

Spatial StatiSticS: Modeling Spatial RelationShipS

14

!P

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!( Sources: Esri, H

ERE, DeLorm

e, USGS, In

termap, in

crement P Corp., N

RCAN,

Esri Japan, M

ETI, Esri C

hina (Hong Kong), E

sri (Thailand), T

omTom,

MapmyIndia, © OpenStre

etMap contributors,

and the GIS User Community

GWR Rail Statio

n Coefficient V

alue

C12_RailDi

!(-0.072023 - -

0.068120

!(-0.068119 - -

0.066363

!(-0.066362 - -

0.065069

!(-0.065068 - -

0.063871

!(-0.063870 - -

0.062282

!(-0.062281 - -

0.057677

!(-0.057676 - -

0.046761

!(-0.046760 - -

0.032140

!(-0.032139 - -

0.028836

!(-0.028835 - -

0.023681

MetroRail

!PRailStation_Pro

0̄

1

2

3

4

0.5

Miles

!P

!P

!P

!P

!P

!P

!P

!P

!P!P

!P!P

!P

!P

!P!P!P!P

!P

!P!P

!P

!P

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GWR LocalR2LocalR2!( < -1.3 Std. Dev.

!( -1.3 - -0.75 Std. Dev.

!( -0.75 - -0.25 Std. Dev.

!( -0.25 - 0.25 Std. Dev.

!( 0.25 - 0.75 Std. Dev.

!( 0.75 - 1.2 Std. Dev.

!( 1.2 - 1.7 Std. Dev.

!( 1.7 - 2.2 Std. Dev.

!( 2.2 - 2.7 Std. Dev.

!( > 2.7 Std. Dev.

MetroRail

!P RailStation_Pro

0̄ 1 2 3 40.5Miles

!P

!P

!P

!P

!P

!P

!P

!P

!P!P

!P!P

!P

!P

!P!P!P!P

!P

!P!P

!P

!P

!(!(!( !(!(!( !(!(!(!(!(!( !(!(!(!(!(!(!( !(!( !(!(!(!( !(!(!( !(!(!( !(!(!(!( !(!( !(!(!( !(!( !(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!( !(!(!( !(!( !(!(!(

!(!(!( !( !(!(!(!( !(!(!(!(!(!( !( !(!(!(!(!( !(!(!(!(!(!( !( !(!(!( !( !(!(!(!(!(!(!(!(!( !( !(!( !(!( !(!(!(!(!(!(!(!(!(!(!(!( !(!(!(!(!( !(!(!( !( !(!(!( !(!(!(!( !(!( !(!(!(!( !(!(!( !(!( !(!(

!(!(!( !(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!( !(!(!(!(!(!(!(!(!(!(!(!(!( !(!(!(!(!( !(!( !(!(!(!(!(!(!(!(!( !(!( !(!(!(!(!(!(!(!( !( !(!(!(!(!(!(!(!( !(!(!( !(!(!( !(!(!(!(!( !(!(!(!(!(!( !( !(!( !(!(!(!( !(!( !(!(!(!( !(!(!( !(

!(!(!(!(!(!( !(!(!(!( !(!(!(!(!( !(!( !(!(!(!(!(!( !(!(!( !(!(!(!(!(!(!(!( !(!(!(!(!(!( !(!(!( !(!( !(!(!(!( !(!( !(!(!(!(!( !(!(!(!(!( !(!(!(!(!( !(!(!(!( !(!( !( !(!(!(!(!(!(!( !(!(!(!(!(!(!(!( !(!(!( !(!( !( !(!(

!( !(!(!(!(!(!(!(!(!(!(!( !(!( !(!(!(!( !(!(!(!( !(!(!( !(!(!(!(!(!(!( !(!(!( !(!( !(!(!(!(!(!(!(!(!(!(!( !(!( !(!(!(!(!( !(!(!(!(!(!(!( !(!( !(!(!( !(!( !(!(!(!(!( !(!( !( !(!( !(!( !(!(!(!(!(!(!( !(!( !(!(!( !(!(!(!(!(!(!(!(!( !(!(!(!(!(!(!(!(!(!(!( !( !( !(!(!(!(!(!( !(!( !(!(!(!(!(!(!(!(!( !(!(!(!(!(!(!(!( !(!( !(!(!(!(!(!(!(!(!(!(!(!(!(!(!( !(!( !(!(!(!( !(!(!(!(!(!(!(!(!( !(!( !(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!( !(!(!(!(!(!(!(!( !(!(!(!(!( !(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!(!( !(!(!(!(!(!(!(!(!(!(!(!(!(!(!( !(!(!(!(!( !(!(!(!(!(!(!(!(!(!( !(!(!(!(!(!(!(!(!( !(!(!(!(!(!( !(!(!(!(!( !(!(!(

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GWR Rail Station Coefficient ValueC12_RailDi

!( -0.072023 - -0.068120

!( -0.068119 - -0.066363

!( -0.066362 - -0.065069

!( -0.065068 - -0.063871

!( -0.063870 - -0.062282

!( -0.062281 - -0.057677

!( -0.057676 - -0.046761

!( -0.046760 - -0.032140

!( -0.032139 - -0.028836

!( -0.028835 - -0.023681

MetroRail

!P RailStation_Pro

0̄ 1 2 3 40.5Miles

GWR model shows that the local residual square is huge, which means that the model

is a good fit for the observed dataset. It

also indicates a large variation between the

observed and predicted just value.

The local R-squre map of the age of buildings, the independent

variable with the strongest spatial explanatory power

Spatial StatiSticS: Modeling Spatial RelationShipS

EstImAtE thE ImPAct Of mIAmI-dAdE mEtRO RAIL systEm ON LANd jUst vALUE

15

cURItIBA

WAshINGtON, d.c.

sAN fRANsIscO

RIO dE jANEIRO

My interests in regional land development and spatial analysis has fostered my love for panoramic views. I traveled and took photographs and notes of various infrastructures and urban development patterns adopted by different countries and cities, and the impact these development have on their citizens.

JINGRU ZHANG | (352)283-2658 • [email protected]

jingru zhang portfolio 2015new

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