using dasymetric mapping to develop a population grid for hazard risk assessments
DESCRIPTION
Using Dasymetric Mapping to Develop a Population Grid for Hazard Risk Assessments. Ben Anderson Project Manager University of Louisville Center for Hazards Research and Policy Development [email protected]. Presentation Outline. Census Data Aggregation Levels - PowerPoint PPT PresentationTRANSCRIPT
BEN ANDERSON
PROJECT MANAGERUNIVERSITY OF LOUISVILLE
CENTER FOR HAZARDS RESEARCH AND POLICY DEVELOPMENT
Using Dasymetric Mapping to Develop a Population Grid for
Hazard Risk Assessments
Presentation Outline
Census Data Aggregation Levels
Problem Statement
Dasymetric Mapping
Population Grid
Objectives
Introduction to Dasymetric Mapping
Application of Dasymetric Mapping
Underlying need for Standardizing Area
Use in Risk Assessment
Census Data Aggregation Levels
Aggregation Levels Country State County Tract Block Group Block
Tracts, Block Groups, and Blocks are aggregation levels that are designed to be similar in population but not area.
Census Tracts
Census Tracts are a statistical subdivision of a county
Tracts are designed to have between 1,500 and 8,000 persons
Tracts can change from Census to Census as the population changes.
In Kentucky Counties have between 1 tract and 191 tracts in the 2010 Census
Census Block Groups
Census Block Groups are a statistical subdivision of Census Tracts
Block Groups are designed to have between 600 and 3,000 people with an optimum size of 1,500
Block Groups can change from Census to Census
Block Groups are the smallest level which the Census bureau publishes sample data As of the 2010 Census all data excluding population
count, sex, age, race, or ownership status is sample data.
Census Blocks
Census Blocks are a statistical subdivision of Block Groups
Blocks in urban areas are often literally a city block, in rural areas blocks can be much larger In KY Blocks range from 94.22 to less than .001
square kilometers. Blocks are the smallest subdivision that the
Census releases full count data on.
Comparing Risk
Center for Hazards Research has done Hazard Risk Assessments Down to the block level for Kentucky.
CHR’s latest state plan relied on count data within blocks to develop a risk score. CHR’s Risk score is a combination of Exposure and Hazard Risk Using pure count data results in a rural bias for increased risk
Increased exposure• Larger blocks may have a higher population but lower density• Larger blocks may also contain more assets: Roads, Rail, Bridges.
Increased hazard risk • Larger Blocks also have more area exposed to a hazard and may
potentially be affected by more incidents due to the increased area
Dasymetric Mapping
Method of mapping population within an aggregation area using population data and land cover data
http://pubs.usgs.gov/fs/2008/3010/fs2008-3010.pdf
USGS Dasymetric Mapping Tool
A free tool which simplifies the Dasymetric process
Requires Land Cover data to be broken down into 4 Classes User defined breaks Suggests –
High Density Low Density Non-Urban Inhabited Uninhabited
Link: http://geography.wr.usgs.gov/science/dasymetric/Or Google: Dasymetric Mapping USGS
USGS Dasymetric Tool Key functions
Empirical Sampling Empirical sampling is used to determine the fraction of the census
unit's population that should be allocated to each inhabited class for the study area
Areal Weighting The ‘population density fraction’ must be adjusted by the
percentage of the block-group’s total area that each ‘inhabited class’ occupies. A ratio is calculated for each ‘inhabited class’ representing the percentage of area that an ‘inhabited class’ actually occupies within a block group to the expected percentage. The area ratio is used to adjust the ‘population density fraction’ accounting for the variation of both the population density and area for the different ‘inhabited classes’ for each block group.
Source: http://geography.wr.usgs.gov/science/dasymetric/data/ToolDescription.docx
Land Cover Data Issues
Low Resolution In non urban areas, there may not be a differentiation in land
class between a residence and the surroundings Resolution is typically able to differentiate roads from
surroundings in rural areas Assumption is population typically lives near roads
Developed Areas that are uninhabited can show as high density development Use Feature Classes to reclassify raster areas to eliminate
developed areas that are uninhabited Highways Airport
Block level data often finite enough that industrial areas are separated from residential areas and show no population
Military Grid Reference System
MGRS could provide an alternative aggregation level to the Census Block level Each unit is identical in size
Allows better comparisons between units in different parts of the state
Population and demographic data is not calculated at MGRS level Assign proportionally based on area Assign using Dasymetric mapping based on area
Grid Advantages For Population Mapping
Grid will enable a like to like comparison of areas across the state rural or urban Population is compared in an equal area Better and easier to view visuals every polygon is
equal area A group of small highly populated blocks (Downtown
areas) will now be as visible as suburban areas.
Kentucky State Hazard Mitigation Plan Risk Assessment
Hazard Vulnerability Score = Exposure Score X Risk Score
Risk Score = Probability of an event X actual consequences (loss) and the % area of each unit that is probable to be affected by an event. The % area is calculated for hazards that have a
defined and predictable spatial extent. For example; Flooding (DFIRM), Karst (KGS), Land Subsidence (KGS), and Landslide (KGS)
Kentucky State Hazard Mitigation Plan Exposure Score
Exposure Score= Population Rank + Property Rank + Essential Facilities Rank + Utility Rank + Transportation Rank + Government-Owned Facilities Rank + Hazardous Materials Rank
Included raw counts and provided a rank (0-3) for each one and then each was added together and ranked again
Next Steps and Issues
Build state wide risk assessment using dasymetric modeling and an equal area grid
Need Better Land Use Data LIDAR
With Buildings Building Footprint data
Better understanding of where population isBetter comparison of different areas
Takes a Census count of population and creates an estimate