Dasymetric Mapping IMPROVING ESTIMATES OF VULNERABLE COASTAL POPULATIONS Presentation by Calvin “Not an Expert” Harmin MS Candidate (2015) East Carolina University Dept. of Geography [email protected] linkedin.com/in/charmin nccoastalatlas.org
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Dasymetric MappingIMPROVING ESTIMATES OF VULNERABLE COASTAL POPULATIONS
Presentation by Calvin “Not an Expert” HarminMS Candidate (2015)East Carolina University Dept. of Geography
[email protected]/in/charmin
nccoastalatlas.org
Disclaimer: I’m no “dasy expert”
But I hope you enjoy this intro to dasymetric mapping!
How do we improve our understanding of where people live?
How could this aid our efforts in emergency management?
How do we know where people live?
Field Work/SurveysUS CensusPopulation sampling
Residential addresses
HOW MANY people
Demographics
statistical estimation within Census boundaries
Bizniss
Census Block
Residential
Census Aggregation Issues
Census Aggregation Issues
Modifiable Areal Unit Problem:
“…the areal units (zonal objects) used in many geographical studies are arbitrary, modifiable, and subject to the whims and fancies of whoever is doing, or did, the aggregating.“
-Dr. Stan Openshaw (1983)The modifiable areal unit problem.
Norwick: Geo Books
Property Information Tax assessors
Parcels
Building information
How else can we know where people live?
Residential square feet
Bedrooms
Apartment units
Building footprints
Most on “developed” land
NOT in water
NOT in fields
NOT in forests (mostly)
Remote Sensing Land Cover
Land use
“Developed”
Dasymetric Mappinghttp://eomag.eu/http://eomag.eu/
Census Data(or other stats)
Ancillary Data(land use/property)
+
DasymetricMap!
=
+ magic
US Census Heirarchy
North Carolina
Counties: 100
Tracts: 2,195
Block groups: 6,155
Blocks: 297,238
US Census Variables to “Dasy-fy”
A wealth of other socio-economic and demographic
variables can be used instead of just “total population”
Disability/Health/Children/Other risk-associated factors
Pets?
However, fewer attributes may be available for blocks
compared to ‘higher’ Census districts.
CENSUS Example – Currituck County
Census Data Sources: www.census.gov; Minnesota Population Center. National Historical Geographic Information System: Version 2.0. Minneapolis, MN: University of Minnesota 2011. http://www.nhgis.org
http://www.nhgis.org is AWESOME!
Census Tracts: 8 Block Groups: 15 Blocks: 741
Total Population ~ 24,000
CENSUS Example – Currituck County
Coastal
Rural
Vulnerable to storm surge and riverine flooding
World Street Map Basemap Total Population ~ 24,000
CENSUS Example – Currituck County
World Street Map Basemap
Currituck Census Blocks
Population
0 - 19
20 - 65
66 - 163
164 - 359
360 - 998
Total Population ~ 24,000
Census block population per acre
CENSUS Example – Currituck County
Census block population per acre
US CENSUS – Currituck County
Currituck Census Blocks
Population
0 - 19
20 - 65
66 - 163
164 - 359
360 - 998
Blocks with zero population? Second homes/tourism?
ESRI Imagery Basemap
Some ‘Empty’ Outer Banks Blocks
LAND COVER – Currituck CountyBackground
Unclassified
Developed, High Intensity
Developed, Medium Intensity
Developed, Low Intensity
Developed, Open Space
Cultivated Crops
Pasture/Hay
Grassland/Herbaceous
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Palustrine Forested Wetland
Palustrine Scrub/Shrub Wetland
Palustrine Emergent Wetland
Estuarine Forested Wetland
Estuarine Scrub/Shrub Wetland
Estuarine Emergent Wetland
Unconsolidated Shore
Bare Land
Open Water
Palustrine Aquatic Bed
Coastal Change Analysis Program (CCAP)
• 2010 data
• Landsat-derived
• 30m pixels
http://coast.noaa.gov/digitalcoast/data/ccapregional
LAND COVER – Currituck CountyCoastal Change Analysis Program (CCAP) http://coast.noaa.gov/digitalcoast/data/ccapregional
Derived from Landsat, like the National Land Cover Dataset (NLCD) but with extra processing for coastal environments.
LAND COVER – Currituck County
Background
Unclassified
Developed, High Intensity
Developed, Medium Intensity
Developed, Low Intensity
Developed, Open Space
Cultivated Crops
Pasture/Hay
Grassland/Herbaceous
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Palustrine Forested Wetland
Palustrine Scrub/Shrub Wetland
Palustrine Emergent Wetland
Estuarine Forested Wetland
Estuarine Scrub/Shrub Wetland
Estuarine Emergent Wetland
Unconsolidated Shore
Bare Land
Open Water
Palustrine Aquatic Bed
Coastal Change Analysis Program (CCAP) http://coast.noaa.gov/digitalcoast/data/ccapregional
ESRI Imagery Basemap
Dasymetric Processing Toolhttp://geography.wr.usgs.gov/science/dasymetric/USGS Dasymetric Tool
Can calculate 3 different population weights for 3 “inhabited” land use classifications:
High/Low/Rural
Some land cover classes need to be combined (subjective).
Rasterization of population polygons
Land Cover ReclassifyingLand Cover ReclassifyingBackground
Unclassified
Developed, High Intensity
Developed, Medium Intensity
Developed, Low Intensity
Developed, Open Space
Cultivated Crops
Pasture/Hay
Grassland/Herbaceous
Deciduous Forest
Evergreen Forest
Mixed Forest
Scrub/Shrub
Palustrine Forested Wetland
Palustrine Scrub/Shrub Wetland
Palustrine Emergent Wetland
Estuarine Forested Wetland
Estuarine Scrub/Shrub Wetland
Estuarine Emergent Wetland
Unconsolidated Shore
Bare Land
Open Water
Palustrine Aquatic Bed
1. High+Medium = High Intensity Urban
2. Low+Open+Bare = Low Intensity Urban
3. Crops+Pasture = Non-Urban
0. All Others Excluded From Pop
Dasy Tool Classes
ArcGIS: Spatial Analyst > Reclass > Reclassify Tool
Reclassification scheme decided from visual inspection in Currituck County. Chose most applicable classes -- those which often contained buildings.
DasyTool Noteshttp://geography.wr.usgs.gov/science/dasymetric/USGS Dasymetric Tool
NEW VERSION: USGS Dasymetric Mapping Tool - ArcGIS 10+ Toolbox (Python)
• Make sure your input datasets are all using the same coordinate and projection information
• Tool runs more efficiently with ArcGRID files• Your Ancillary Raster Land Use file should be in a thematic format, NOT
continuous.
Beta version… sort of broken (as of 2/2015)
Beta version requires you to rename your feature/raster files names and field names to match the python script. Or edit the script. Still workable, hopefully will be updated soon.
Dasymetric Processinghttp://geography.wr.usgs.gov/science/dasymetric/USGS Dasymetric Tool
Dasymetric Processinghttp://geography.wr.usgs.gov/science/dasymetric/USGS Dasymetric Tool
Census blocks
CCAP land use (reclassified)
Census block unique ID field
Census block population value field
Magical empirical sampling for land use “weighting”
See http://geography.wr.usgs.gov/science/dasymetric/data/methods.pdf
Dasy Output Comparison“persons per acre” vs “persons per pixel”
Dasy raster ouput – stretch 2.5 std devCensus blocks with land
Comparing 100-yr Flood Zone IntersectDasy raster ouput – stretch 2.5 std devCensus blocks with land
Comparing 100-yr Flood Zone IntersectDasy raster ouput – stretch 2.5 std devCensus blocks with land
Maantay, J., & Maroko, A. (2009). Applied Geography, 29(1), 111-124.
Mapping urban risk: Flood hazards, race, & environmental justice in New York
Dasy in the Literature – Other Methods
No Land Cover
Use parcel data with building information like residential ft2.
Use improved estimates of flooded residences to investigate E.J issues.
• Dasymetric methodology / uncertainty• (Mennis, J. 2003)• (Maantay, J. A., Maroko, A. R., & Herrmann, C. 2007)• (Petrov, A. 2002)• (Nagle, N. N., Buttenfield, B. P., Leyk, S., & Spielman, S. 2014)
• crime mapping • (Bowers & Hirschfield, 1999; Poulsen & Kennedy, 2004)
• accessibility measures in health studies • (Langford & Higgs, 2006)
• environmental justice and health research • (Maantay, J., & Maroko, A. 2009)• (Maantay, Maroko, & Porter-Morgan, 2013)
• Identifying socioeconomic/environmental risk patterns• (Parrott et al., 2007)
• facilitate accessibility measures • (Linard, Gilbert, Snow, Noor, & Tatem, 2012)
Dasy in the Literature – A Small Sample
Road Networks are often “developed” land use classes Filtering/aggregating can remove many roads, but also lose ‘valid’ cells Pre-processing road networks out of land cover model can improve this
Major Issues with Dasy
Unfiltered Aggregated to 60m
No standardized methodology Every study does it differently?
30 meter raster too coarse to capture rural homes
Difficult to get salient property data Digital records not standardized between counties
Major Issues with Dasy
Problems aside, dasy techniques readily increase accuracy of estimated population within “areas of interest” (e.g. hazard overlay).
Bottom Line
Not everyone will need such accurate population density data, but the potential value and use of should be investigated further.
Once dasy methods mature more, higher resolution global land use change data may be ubiquitous
Enhance population estimation of remote areas? Disaster assessment?
Enhance tracking and modeling of urban change Sprawl / Climate change refugees?
Going Global?
Please Comment / Question
Special thanks to NC GIS Organizers and Workers
My advisor Dr. Tom Allen, Rob Howard, and Herbert Stout
Calvin “I’m Looking for a Job” HarminECU - MS Geography (2015)
[email protected]/in/charmin
How do you think improved population data might be used, and by whom?
How do you decide “where people are at risk” for hazard studies?
Hospital populations? Night time vs. Day time?
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