1. SPATIAL ANALYSIS FOR SUSTAINABLE URBAN DEVELOPMENT
DALLAS – FORTWORTH – ARLINGTON FRINGE
Sanaul Huq
2. INTRODUCTION
Kessler, City plan
for Dallas, 1911
“art, architecture,history,
nature and citizenship”
The concept of sustainable development
to USA and Dallas is not
New
2015
“Sustainable development is the
greatest, most complicated
challenge humanity has ever faced”
1961
3. REVIEW OF NOTABLE WORKS AND PERSONALITIES
Michhael MannJean Gottman Paul Waddell
Ebnezer Howard
19351930
Zaha Hadid 2001 - 2021
Sir Patrick Geddes Jane Jacobs 1961
Ian Mc Harg 1985
19031853 1921
Le Corbusier Frank LloydWright
1968 1999 2010 2011
4. RATIONALE FOR THE PROJECT
Observations
Urbanization is a significant trend of the 21st century
DFW Metroplex is one of the more rapidly developing urban areas
Interest on spatial data is on the rise
Geographic Information System (GIS) is playing an important role
Impact on participation is greater when maps and visuals are presented
Potentials
Integrating sustainability with urban design may provide good solutions
Spatial Analysis method of ArcGIS and its features can be an effective tool.
5. BACKGROUND
DFW Metroplex : At a Glance
Estimated employment growth of the fastest growing counties(2035)
o Kaufman County 86%
o Johnson County 83%
Finance
o Metroplex to need $ 350 billion to eliminate congestion
o Has a growth, development and land use budget of about 4 billion
Demographics of the North Central Texas Counties (16)
o Population grew 25% (2000 – 2010)
o Forecast from 6.5 m to 9.8 m (2035)
o Population density to increase from 718 to 1042 persons per sq. mile
Social Mobility
o Low level of community involvement
o Poverty has increased from 11.9% to 13.9% (2000 – 2010)
o Environmental Justice score 63% are below unprotected ( accessible jobs are
more than 60 minutes away by transit)
Cities Ranked by
Population
Year of
Highest
Rank
Highest
Rank
Present
Rank: 2013
Boston 1740 1 24
Philadelphia 1800 1 5
Baltimore 1820 3 26
Rochester 1840 13 103
Buffalo 1850 10 73
St. Louis 1870 4 58
Detroit 1930 4 12
6. TRENDS THAT ARE OF CONCERN
Buildable lot supply is
going below the
equilibrium level
Low inventories and
strong Texas economy
have spurred demand
Variations in property value,
energy use and % of income
on rent by block groups
7. OBJECTIVE
This thesis is in the area of sustainable urban development and
‘Locational Intelligence’.
Sustainability is concerned with activities “that meets the needs of the
current generations without compromising the ability of forthcoming
generations to meet their own needs”.
The objective of this thesis is to examine if sustainable
development problems of multi-city regions can be addressed
through spatial analysis.
8. RESEARCH QUESTIONS
Which cites of the DFW Metroplex are the low-hanging-
fruits for sustainable urban development ?
How could changes in the distribution of population
growth make cities more sustainable?
Can this growth be directed to where it both contributes
to socio-economic development and inflict the least
environmental impact ?
9. THIS PROJECT …..
IS
• A contribution in ‘Locational Intelligence’ service.
• An opportunity to visualize and assess sustainability methods
• An exercise in synthesis and model development
• A chance to explore how modelling tools can fit into the land use
planning process
IS NOT
• An attempt to predict future land use
• A planning tool for any agency or organization
• An analysis of any immediate proposed project
10. METHODOLOGY
Assumptions
o DFW Metroplex is growing at the edges
o Real Estate developers are considering options other than sprawls (rise in multifamily and zero-lot construction)
o Location choice for employers and household is determined by accessibility
Spatial Analysis as a tool
o History of success
o Has been tried successfully in Urban Design
o Not applied to multi-city sustainable development settings
o When applied to sustainable development for multi-cities may provide simple solutions
Geodatabase
o Take inventory of existing data
o Collate and determine gap
o Download and develop geodatabase
o Mine and create
o Validate
11. …… methodology contd
Step 1: Set query criteria and filter out cities with high potentials
Step 2: Determine shape of directional distribution
o Create centrographic ellipses
o Find dispersion between employment , developments and population distribution
Step 3: Pattern analysis
o Based on 1st principle of Geography – ‘near things are more related than distant things’
o Determine if suitable parcels lots are either random, dispersed or clustered through Outlier method
o Find hot spots through the Getis-Ord Star method
12. PATTERN ANALYSIS
Find energy index data
Filter structures on lots built before 1973
Integrate Floor Area Ration (FAR) data
Blende table to filter suitable parcels for redevelopment
13. BASIS OFTHE ANALYSIS : PARCELS
Framework
• The four D’s
o Demographic
o Data driven
o Dynamic
o Directional distribution
• Analysis
o Dependent on statistical results
o Integrates employment,
household, and environment
o Considers cluster of parcels
o Concurrent suitability
assessment
14. DFW AND ITS FRINGE MUNICIPALITIES
35
Querying is an elementary
function of GIS. A query searches
the GIS database for features that
meet certain user-defined criteria.
Dallas-FortWorth-Arlington
15. FILTERING FOR CANDIDATE TARGET CITIES
Criteria set for
the Query tool
• Population > 7000
• Population Growth >2.75 %
• Transportation budget > 70
• Gross Density < 1650
persons per sq mile
• Area more > 5,000 acres
• LandWater Ratio > .01 %
169 JURISDICTIONS 4 CITIES
17. DIRECTIONAL DISTRIBUTION
Centrographics
o The tool creates a new feature class containing an
ellipse centered on the mean center for all features.
o Output include two standard distances :long and
short axes.
o With features normally distributed around the mean
center, the ellipse will cover most of the input
features.
19. ……centroid analysis contd.
Difficult to
compute
Major difference
between employment
and population growth
Directional distribution of employment , developments and population growth
20. DENTON : POTENTIAL LOCATIONS FOR DEVELOPMENT
Legend
Denton_Empl...
FARM_CLASS
All areas are
prime farmland
Not prime
farmland
Denton_Publi...
Denton_Direct...
Employers_M... 0 0.75 1.5 2.25 30.375
Miles
1 in = 1 miles
YDenton Directional Distribution
21. PATTERN ANALYSIS
Used to identify and quantify spatial patterns.
Examines spatial characteristics through
o The Average nearest neighbor tool calculates how close features are
located to each other
o The Getis-Ord General tool determines whether areas of similar
values are more clustered
o The Multi-distance clustering tool examine multiple distances other
than the next nearest feature.
o The Spatial autocorrelation tool combines evaluation of clustering by
location and value Also used for hypothesis testing
22. OUTLIER ANALYSIS AND CLUSTERING
Outlier Analysis showing clustering by location
and by values comparable in magnitude
Areas of possible
Clusters
Pattern Analysis
overlaid with Land use
Getis-Ord General tool Multi-distance clustering tool Spatial autocorrelation tool
23. PATTERN ANALYSIS TO IDENTIFY HOT SPOTS
Query Criteria for Hot Spot Analysis
• Developed before 1973
• One acre plus size
• Floor Area Ratio (FAR) less than 20 %
From 2014 Parcel Data Autocorrelated Hot Spots
1,275
Lots
8,588
Acres
26. ……… selected clusters contd.
7
8
Of the 1275 target parcels
o Some are randomly scattered
o A good number of them are clustered
o Some of those clusters are not suitable
for sustainable development
o There are suitable clusters for
potential higher density developments
27. PARTIAL TABLE
FEDERAL
ID OWNER_NAME ADDRESS LINE2 ZIP LOT CODE YEAR BUILT AREA IN ACRES
143 SHIELDS, JAMES A & KUUSI-SHIELDS, MERI T 2900 MONTECITO DR 76205 24 A1 1969 2.02
152 MORGAN, DERRICK P 3300 CARMEL ST 76205 3 A1 1969 1.14
175 SPENCE, MAYNARD R & KATHRYN C 2810 MONTECITO DR 76205 25 A1 1969 2.22
244 CABALLERO, ISRAEL & NATALIA MARENALES 1907 N LARIAT RD 76207 8 A1 1969 2.15
267 STAPLES, KRISTEN P & DONALD E 2901 MONTECITO DR 76205 16 A1 1969 1.44
276 MALTZ, TIMOTHY M & ANDREA M 2801 CARMEL ST 76205 1 A1 1969 1.54
284 FOUST, JOHN E IV & DONNA R 3000 CARMEL ST 76205 9 A1 1969 1.43
290 DECK, STEVEN B & LINDA S 2012 CINDY LN 76207 E1 1969 3.85
317 BRYANT, RICKY D PO BOX 2943 76202 12A A1 1969 1.97
351 RILEY, TONY A & LINDA P 3806 W UNIVERSITY DR 76207 1 A1 1969 2.15
31 COWARD, GREG & JULIE LYNN 624 EL PASEO ST 76205 1 A1 1968 1.73
48 KLEINKAUF, ROBERT W & JULIE L 1918 N LARIAT RD 76207 15 A1 1968 3.91
61 JOHNSON, ROBERT E 600 EL PASEO ST 76205 2 A1 1968 1.85
144 BLACK, STEVEN M & ELIZABETH L 3201 CARMEL ST 76205 1 A1 1968 1.68
145 MURRAY, MICHAEL D & SUSAN A 3211 SANTA MONICA DR 76205 10 A1 1968 2.24
147 SILVAGGIO, MICHAEL A & TINA A 3400 CARMEL ST 76205 1 A1 1968 1.25
29. CONCLUDING REMARKS AND FINDINGS
Common Characteristics
o Within a high growth region
o Part of a reasonably sized city
o Has high transit potential
o Close proximity to employment
o Clustered large lots (one acre +)
o Built before 1973 ( Low Efficient
Buildings )
o Of little historical significance
o Low floor area Ratio
o Not prime farmlands
o Not within wetlands
o Have low erosion risk
30. LIMITATIONS AND FUTURE RESEARCH DIRECTIONS
Limitations
Its effectiveness in a collaborative environment have not been tested
The research limited to a number of processes
The tool does not address all the functionalities of sustainable design
Land value and costs were not considered
Next Steps
Integrate with like Google Earth, Esri City Engine and emerging trends
Sketchup schematic designs for comparison and communication
Conduct qualitative evaluation through the social media
