Modeling Urban Sprawl: From Raw TIGER Data with GIS
Modeling Urban Sprawl: from Raw TIGER Data with GIS Brady Foust University of Wisconsin-Eau Claire Lisa Theo University of Wisconsin-Stevens Point
Modeling Urban Sprawl
1
Problem • How to model & predict urban expansion. • Plan for: City services. Schools. Traffic flows.
Modeling Urban Sprawl
2
Traditional Methods • • • • • • •
Population density. Building density. Building permits. Utility hookups. All are difficult and expensive to collect. Hard to construct a time sequence. Most are “near real time”.
Modeling Urban Sprawl
3
Borchert's Methodology • Borchert, John “The Twin Cities
Urbanized Area: Past, Present, Future”, Geographical Review, Vol. 51, No. 1 (January 1961), pp. 47-70. • Counted street intersections per square mile off 7.5” topographic sheets. • Key idea = development follows roads. • Problems:
Sheets can be old. Adjacent sheets published at different times.
Modeling Urban Sprawl
4
Modeling Urban Sprawl
5
This Paper • Examines the use of raw Bureau of the • • •
Census TIGER data to model urban sprawl. Thesis: Network density is an excellent way to anticipate urban infilling. Annual updates of TIGER provide historical data that can be projected ahead using geostatistical analysis
Modeling Urban Sprawl
6
TIGER • Now released at least annually. • Constant updating (positional accuracy). • Constant updating (new roads/streets). • Provides basis for determining intersections per square mile.
Modeling Urban Sprawl
7
TIGER Basics • File of nodes. • Create lines and polygons with “connect the dots” routines using underlying database. • Intersections = multiple nodes in same location.
Modeling Urban Sprawl
8
Turn (shape point)
Intersection
Intersection
Modeling Urban Sprawl
9
Intersection "Rules" • Must be a street node (feature) • Three or more street nodes with same location (lat/lon) = intersection. • Grid for calculating intersections per square mile must be clipped with water features.
Modeling Urban Sprawl
10
Raw TIGER • • • • • • • •
Drop base file into dBase. Delete all non-street features. Concatenate lat/lon (alpha). Convert lat/lon to text (LATLON). Concatenate. Total on LATLON to new file. Delete any record where count < 3. De-concatenate LATLON to two numeric fields. • Create event layer.
Modeling Urban Sprawl
11
Intersections Eau Claire, WI Metropolitan Area
Each dot = 1 intersection
0
3
6
Miles
Modeling Urban Sprawl
12
Intersections per Sqare Mile • • •
Generate grid (1 mile, ¼ mile, etc.) Spatial join to obtain per cell count. Problem – have to clip grid where housing is impossible (lake, river, ocean).
Modeling Urban Sprawl
13
Each cell = on square mile. Each cell has 64 intersections. Intersection density = 64 per square mile
= 1.0 Mi2
= 0.20 Mi2
Dismal Lake
8 intersections per MI2
Modeling Urban Sprawl
14
40 intersections per MI2
Intersections Eau Claire, WI Metropolitan Area
Each dot = 1 intersection
0.24 Mi2
0
1
2
Miles
Modeling Urban Sprawl
15
TIGER Intersections per Square Mile
1 Mile Grid Categories Rural Fringe Urban 0
5
10
Miles
Modeling Urban Sprawl
16
Intersections Per Sq. Mile Simple Kriging
Intersections Per Sq. Mile Simple Kriging
0 - 10 10 - 30 30+ 0
2
4
8
Miles
Modeling Urban Sprawl
17
Distance Decay in Intersections per Square Mile 180
Intersections per Square Mile
160
140
120
100
80
60
40
20
0 0.0
5.0
10.0
15.0
Distance from City Center
Modeling Urban Sprawl
18
20.0
25.0
30.0
LAS VEGAS Change in Street Intersections per Square Mile 1992-2006
Sources: TIGER 1992, 2006 Legend 10 25 50 75 100 < 10 10 - 25 25 - 50 50 - 75 75 - 100 100 - 171
Modeling Urban Sprawl
19
LAS VEGAS Change in Street Intersections per Square Mile 1992-2006
Sources: TIGER 1992, 2006 Legend 10 25 50 75 100 < 10 10 - 25 25 - 50 50 - 75 75 - 100 100 - 171
Modeling Urban Sprawl
20
TIGER • Now released at least annually. • Constant updating (positional accuracy). • Constant updating (new roads/streets). • Provides basis for determining intersections per square mile.
Modeling Urban Sprawl
21
Census • Decision to release TIGER in shapefile • • • •
format. Shapefiles = defacto standard. “What everyone wants”. No plans to release raw TIGER data any longer. Intersections not easily obtained.
Modeling Urban Sprawl
22
What now? • Improvisation = key GIS skill. • Could convert line file to nodes using ArcInfo routine. • Roads may be a better predictor of sprawl than simple intersections.
Modeling Urban Sprawl
23
Intersections = 0
Modeling Urban Sprawl
Intersections = 2
24
Modeling Urban Sprawl
1
Problem • How to model & predict urban expansion. • Plan for: City services. Schools. Traffic flows.
Modeling Urban Sprawl
2
Traditional Methods • • • • • • •
Population density. Building density. Building permits. Utility hookups. All are difficult and expensive to collect. Hard to construct a time sequence. Most are “near real time”.
Modeling Urban Sprawl
3
Borchert's Methodology • Borchert, John “The Twin Cities
Urbanized Area: Past, Present, Future”, Geographical Review, Vol. 51, No. 1 (January 1961), pp. 47-70. • Counted street intersections per square mile off 7.5” topographic sheets. • Key idea = development follows roads. • Problems:
Sheets can be old. Adjacent sheets published at different times.
Modeling Urban Sprawl
4
Modeling Urban Sprawl
5
This Paper • Examines the use of raw Bureau of the • • •
Census TIGER data to model urban sprawl. Thesis: Network density is an excellent way to anticipate urban infilling. Annual updates of TIGER provide historical data that can be projected ahead using geostatistical analysis
Modeling Urban Sprawl
6
TIGER • Now released at least annually. • Constant updating (positional accuracy). • Constant updating (new roads/streets). • Provides basis for determining intersections per square mile.
Modeling Urban Sprawl
7
TIGER Basics • File of nodes. • Create lines and polygons with “connect the dots” routines using underlying database. • Intersections = multiple nodes in same location.
Modeling Urban Sprawl
8
Turn (shape point)
Intersection
Intersection
Modeling Urban Sprawl
9
Intersection "Rules" • Must be a street node (feature) • Three or more street nodes with same location (lat/lon) = intersection. • Grid for calculating intersections per square mile must be clipped with water features.
Modeling Urban Sprawl
10
Raw TIGER • • • • • • • •
Drop base file into dBase. Delete all non-street features. Concatenate lat/lon (alpha). Convert lat/lon to text (LATLON). Concatenate. Total on LATLON to new file. Delete any record where count < 3. De-concatenate LATLON to two numeric fields. • Create event layer.
Modeling Urban Sprawl
11
Intersections Eau Claire, WI Metropolitan Area
Each dot = 1 intersection
0
3
6
Miles
Modeling Urban Sprawl
12
Intersections per Sqare Mile • • •
Generate grid (1 mile, ¼ mile, etc.) Spatial join to obtain per cell count. Problem – have to clip grid where housing is impossible (lake, river, ocean).
Modeling Urban Sprawl
13
Each cell = on square mile. Each cell has 64 intersections. Intersection density = 64 per square mile
= 1.0 Mi2
= 0.20 Mi2
Dismal Lake
8 intersections per MI2
Modeling Urban Sprawl
14
40 intersections per MI2
Intersections Eau Claire, WI Metropolitan Area
Each dot = 1 intersection
0.24 Mi2
0
1
2
Miles
Modeling Urban Sprawl
15
TIGER Intersections per Square Mile
1 Mile Grid Categories Rural Fringe Urban 0
5
10
Miles
Modeling Urban Sprawl
16
Intersections Per Sq. Mile Simple Kriging
Intersections Per Sq. Mile Simple Kriging
0 - 10 10 - 30 30+ 0
2
4
8
Miles
Modeling Urban Sprawl
17
Distance Decay in Intersections per Square Mile 180
Intersections per Square Mile
160
140
120
100
80
60
40
20
0 0.0
5.0
10.0
15.0
Distance from City Center
Modeling Urban Sprawl
18
20.0
25.0
30.0
LAS VEGAS Change in Street Intersections per Square Mile 1992-2006
Sources: TIGER 1992, 2006 Legend 10 25 50 75 100 < 10 10 - 25 25 - 50 50 - 75 75 - 100 100 - 171
Modeling Urban Sprawl
19
LAS VEGAS Change in Street Intersections per Square Mile 1992-2006
Sources: TIGER 1992, 2006 Legend 10 25 50 75 100 < 10 10 - 25 25 - 50 50 - 75 75 - 100 100 - 171
Modeling Urban Sprawl
20
TIGER • Now released at least annually. • Constant updating (positional accuracy). • Constant updating (new roads/streets). • Provides basis for determining intersections per square mile.
Modeling Urban Sprawl
21
Census • Decision to release TIGER in shapefile • • • •
format. Shapefiles = defacto standard. “What everyone wants”. No plans to release raw TIGER data any longer. Intersections not easily obtained.
Modeling Urban Sprawl
22
What now? • Improvisation = key GIS skill. • Could convert line file to nodes using ArcInfo routine. • Roads may be a better predictor of sprawl than simple intersections.
Modeling Urban Sprawl
23
Intersections = 0
Modeling Urban Sprawl
Intersections = 2
24
