Final Project: “Use of GIS in Selecting Urban Areas”
GEOGRAPHIC INFORMATION SYSTEMS
Geographic Information Systems
Final Project
”Use of GIS in Selecting Urban Areas”
Presented by
Jesús Del Río
UNIVERSITY OF NEW MEXICO
May 6th, 2012
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GEOGRAPHIC INFORMATION SYSTEMS
Final Project: “Use of GIS in Selecting Urban Areas”
1.0. INTRODUCTION
1.1. BACKGROUND
The use of Geographic Information Systems has substantially increased in recent years. The
characteristics of data obtaining, manipulation and analysis make it suitable in many fields that require solving
spatial problems. Applications and programs have been developed for establishing accurate methods of
processing information. Newer technologies are being developed for providing examination of previously
unknown data. GIS represent a conjunction of interdisciplinary sciences that provide powerful tools for
understanding what is on earth and below its surface.
Urban areas constitute a concentration of diverse landscaping that encompasses large areas of land. The
analysis and processing of the infrastructure of any city may represent a challenging task. Geographic
coordinates, altitude and land use types are examples of data needed for controlling and understanding these
complex and evolving areas.
The city of Albuquerque - with its approximate 181 square miles and almost
600,000 habitants - represents a convenient model for this research. In addition, GIS
resources are readily available, moderate file size and inexpensive.
1.2. OBJECTIVE
To use all the GIS data available for examining the capacities, quality and extent to perform a
straightforward analysis of a city for selecting an urban area with specified characteristics.
1.3. MOTIVATION
Joining construction management, urbanism, city planning and real estate fields for building appropriate
criteria to select areas that may reduce traffic, promote the use of public transportation and reduce cost of
construction. The promotion of community welfare is a byproduct of this motivation.
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2.0. METHOD
2.1. DATA SOURCES
The information processed was obtained free of cost from the following sources:
a) Bernalillo County Assessor’s Office through its GIS department1.
b) City of Albuquerque government through its GIS website2.
c) UNM Bureau of Business and Economic Research3.
d) U.S. Census Bureau4.
2.2. GEOGRAPHIC SYSTEM AND PROJECTIONS USED
All information used possesses the following characteristics:
Coordinate System:
State Plane (SPCS) HARN for New Mexico.
Projection:
Transverse Mercator.
Datum:
NAD83
2.3. DESCRIPTION
The State Plane Coordinate System (SPCS) is a set of 124 areas designated only for regions in the
United States of America. It ignores the curvature of the earth making calculations easy and fast. Each state
contains different plane zones that are highly accurate within these relatively small zones but are very
inaccurate beyond their boundaries. These plane zones are suitable for areas as small as counties. The
inconvenience of this coordinate system occurs when counties in different states have to be analyzed in the
same context, as transformations may be complex.
The projection used is adequate in zones that are long in the north-south direction as this type of
1
GIS database provided for this research free of cost. This data is usually sold to users.
2
City of Albuquerque website GIS section: http://www.cabq.gov/gis/.
3
. UNM Bureau of Business and Economic Research website http://bber.unm.edu/
4
U.S. Census Bureau sites: http://factfinder2.census.gov/faces/nav/jsf/pages/index.xhtml and http://www.census.gov/geo/www/tiger/tgrshp2010/tgrshp2010.html
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Final Project: “Use of GIS in Selecting Urban Areas”
projection is better at maintaining accuracy along a north-south axis.
A geodetic datum is the mean to correlate a coordinate system to the earth. In this case, the North
American Datum of 1983 was used. Since GIS technologies have improved in accuracy, the additional term
HARN refers to High Accuracy Reference Network, a module of information used to increase precision for
previous datum.
2.4. SOFTWARE ENVIRONMENT
To perform this type of analysis the ArcMap editor level is needed with at least the Spatial Analyst
license active. ArcView does not work as an analysis tool and ArcMap at the Info level provides more licenses
than needed. In addition, an ArcInfo license is more useful as it has access to powerful tools.
The files used for this analysis were shape files. These vector files are the results of years of land surveying and
database collection. Photogrammetric and raster data are not relevant for this research as different areas
represent discrete regions (polygons) instead of continuous and smooth surfaces.
3.0. ANALYSIS
3.1. TARGET CONDITIONS
3.1.1. CHARACTERISTICS OF THE AREA
Select a specific area within the city with the following conditions:

Commercial, educational and recreational services within walking distance

In an area of $100,000 to $200,000 of average property value
3.1.2. CHARACTERISTICS OF THE INVESTMENT
A construction of 10 duplex family units (20 housing units total) in the area selected with the following
characteristics:
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
3 bedrooms
Living

1.5
Bathrooms
room, dining room and kitchen
Sales price of approx. $140,000 per unit
1-2
2

Parking spaces per unit
floors. 100 m2(1,100 sq. ft.) each.
For
sale or lease
Duplex configuration for residential financing
3.1.3. CHARACTERISTICS OF THE BUYER
The people targeted for this investment will have to meet the following characteristics:
Family
Level
of 2 adults and no more than 2 children
of income of Adults: $70,000 or more (single or combined)

Looking

Owns 1 or 2 cars

Willing to use public transportation
for a quiet place to live with services within walking distance
3.2. SELECTION SYSTEM
The simplest and easiest way is to select areas by neighborhood. These areas have been naturally
organized in accordance to their inhabitants economic level and preference of living urban area type. According
to the study of Metropolitan Growth and Neighborhood Segregation by Income (Watson, T., 2005): “Between
1970 and 2000, residential segregation by income has become an increasingly important feature of the
metropolitan landscape in the United States. About two-thirds of American metropolitan areas witnessed
increasing segregation of the rich from the poor over the last three decades. Over 85 percent of the
metropolitan population lives in an area that was more segregated by income in 2000 than in 1970.” So
grouping the city regions by neighborhood is a wise method of selection.
3.3. CRITERIA
The analysis performed by ArcMap used to the following criteria in classifying neighborhoods
by weight of importance:
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Final Project: “Use of GIS in Selecting Urban Areas”
Criteria One: Average property value by neighborhood (10%). The property values were provided in the base
map that the Bernalillo County Assessor’s office used for calculating property taxes in 2012. It is important to
mention that these values are just estimates developed at assessor’s office. Real values may differ dramatically
in some cases.
Figure 1. Assessor’s office property estimates.
Criteria Two: Less payment of taxes. Taxes in the city of Albuquerque are calculated in the following manner:
Net Taxable Value = Estimated Total Value (Gross Taxable Value)
3
Property Tax = (Net Taxable Value – Exemptions)*Mill Rate
1000
Where Mill rate is the proportion of all governmental assessments. See figure 2 on next page:
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Figure 2. Breakdown of mill rate for the city of Albuquerque.
According to this method, all properties in the city pay the same proportion of taxes. Therefore any area in the
city is suitable for construction.
Criteria Three: Percent of residential areas (30%). The residential areas were selected by the code specified by
the city. All residential areas were added and divided by the total area by neighborhood for obtaining the
residential fraction.
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City of Albuquerque Land Use Codes
1000 RESIDENTIAL-ALL OTHER
1111 SINGLE FAMILY RESIDENTIAL
1112 MOBILE HOME (NOT IN A COURT)
1113 MOBILE HOME ATTACHED TO AN OTHER USE
1120 TOWNHOUSE
1121 S.F. HOUSE ATTACHED TO A COMMERCIAL USE
1122 S.F. HOUSE ATTACHED TO A INDUSTRIAL USE
1123 S.F. HOUSE ATTACHED TO A AGRICULTURAL USE
1211 MULTI-FAMILY 2 UNITS
1222 MULTI-FAMILY 3-4 UNITS
1233 MULTI-FAMILY 5-9 UNITS
1244 MULTI-FAMILY 10-19 UNITS
1255 MULTI-FAMILY 20-49 UNITS
1266 MULTI-FAMILY 50 OR MORE UNITS
1350 MOBILE HOME PARK - UNDETERMINED SIZE
1351 MOBILE HOME PARK 1-10 SPACES
1352 MOBILE HOME PARK 11 OR MORE SPACES
1411 BOARDING HOUSE
Figure 3. Residential Codes. Only highlighted concepts were considered.
Criteria Four: Closeness to Landfills. The neighborhoods that were 1,000 m (3,280 ft.) from a closed or active
landfill were removed from all lists.
Criteria Five: Number of bus stops (20%). Creating a selection based on the distance range was overwhelming
and led to wrong conclusions. A simple count of bus stops by neighborhood was performed. These criteria are
supported by the idea that long walking distances increases vehicle usage. According to the study of “Walking
Distances to and from Light-Rail Transit Stations”, most people will not walk more than 1,750 m (5,740 ft.) to
reach a station. Furthermore, these walking distances are strongly influenced by efficiency of the public
transportation and the characteristics to access it. (O’Sullivan, S. & Morrall, J., 1996).
Criteria Six: Number of schools. Only neighborhoods that have at least two schools within their boundaries
were selected. This narrowed the result making the search simpler. Information provided in shape files.
The selection of areas in accordance to criteria shown below was performed in the same manner selection was
made for criteria five:
Criteria Seven: Number of parks (10%). Information provided in shape files.
Criteria Eight: Number of grocery stores. (10%).
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2300 FOOD, LIQUOR, AND CONSUMABLES-ALL
OTHER
2311 SUPERMARKET-GROCERY
2312 NEIGHBORHOOD-GROCERY
2313 CONVENIENCE STORE
2314 DELI-GROCERY
2321 BUTCHER SHOP
2323 FISH MKT.
2324 FRUITS AND VEGETABLE STORE
2325 FRUITS AND VEGETABLE STORE (OPEN STAND)
2326 DAIRY PRODUCTS
Figure 4. Codes for selecting grocery stores. Only highlighted concepts were considered.
Criteria Nine: Number of Drugstores (5%).
2130 MUSIC STORE
2131 DISCOUNT DRUGSTORE
2132 DRUGSTORE
2141 MAIL ORDER OUTLET
2150 PAWN SHOP/TRADING POST
2151 PET SHOP
Figure 5. Codes for Selecting Drugstores. Only highlighted concepts were considered.
3.4. RESULTS REVIEW
The level of income by housing unit price was obtained for the neighborhood ranked number one. The
information was obtained from the U.S. Census Bureau using shape files provided in the TIGER site.
3.4. TOOLS USED.
Several tools were used in this research, the most relevant are:
Select by attributes and location: Select features in a specified location or with a specific characteristic.
Spatial Joint: Creates a new layer joining features that occupy the same space and all their characteristics
providing a count and statistical results based on the functions selected.
Multiple rings buffer: Provides circular areas which center is any point feature selected.
Feature to point: Converts features to points based on their centroids location.
Clip: Removes or cuts features using a feature as clip.
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4.0. RESULTS
A total of 177 Neighborhoods were analyzed. Prospective neighborhoods results were and analyzed in EXCEL
using several functions. The counting and final selection was made manually.
Criteria One
Table 1. Average value versus number of neighborhoods.
Rio
Grande
Figure 6. Distribution of neighborhood average value.
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Criteria Three
Table 2. Percent of residential areas versus number of neighborhoods.
Rio
Grande
Figure 7. Distribution of percent of residential areas.
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Criteria Four
Table 3. Neighborhoods close to landfills at 1,000 m (3,280 ft.) or less.
Rio
Grande
Figure 8. Distribution of neighborhoods close to landfills.
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Criteria Five
Table 4. Bus stations versus number of neighborhoods.
Rio
Grande
Figure 9. Distribution of neighborhoods by number of bus stops.
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Criteria Six
Table 5. Number of schools versus number of neighborhoods.
Rio
Grande
Figure 10. Distribution of neighborhoods by number of schools.
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Criteria Seven
Table 6. Number of parks versus number of neighborhoods.
Rio
Grande
Figure 11. Distribution of neighborhoods by number of parks.
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Criteria Eight
Table 7. Number of grocery stores versus number of neighborhoods.
Rio
Grande
Figure 12. Distribution of neighborhoods by number of grocery stores.
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Criteria Nine
Table 8. Number of Drugstores versus number of neighborhoods.
Rio
Grande
Figure 13. Distribution of neighborhoods by number of Drugstores.
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Selection
Table 9. Final Results. Criteria 2, 4, and 6 were also considered for this selection.
Figure 14. Census tract for the neighborhood ranked number one.
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Table 10. 2009 American Community Survey Results for Tract 2.05. McKinley Neighborhood.
5.0. CONCLUSIONS.
GIS is powerful in analyzing urban areas as it can process a large amount of geographic information in
respect with land use including many characteristics for each feature. GIS analysis requires great quantities of
information. Without data, analyses cannot be performed. It is crucial to research all information available prior
to conducting any research.
Special importance should be given to the quality of the information. If information is incomplete or
missing the analysis may lead to incorrect conclusions. For many cases in this research, mistaken codes of land
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use in features, and slight differences in coordinates led to errors in counting and selecting. In addition, criteria
for analysis should consider factors that may determine the results. As an example, for criteria one, the total
area of the neighborhood included streets, parks and different infill areas. These criteria should be analyzed in
detail as these areas may alter the residential to total neighborhood area ratio. A narrower analysis, perhaps at a
block level will be the optimum. This was not conducted for lack of time and accuracy in data by block. Table
10 shows the results provided in the American Community Survey (ACS); these results are just an estimate
based on a population sample and may not represent the current conditions of the area. Although, the ACS
results are estimates, it is possible to observe that as predicted, the majority of household income by unit
matches the description of the number one rank neighborhood. A more refined query with current results from
the census is required to make proper conclusions.
The knowledge regarding spatial tools greatly influences how the time data is processed and its final
quality. Perhaps some analyses can be conducted with basic tools but may be done more efficiently with the use
of advanced tools.
The data used should be examined carefully and its veracity confirmed. As databases contain large
batches of information, is easy to alter them with improper manipulation.
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6.0. REFERENCES
O’Sullivan, S. & Morrall, J. (1996). Walking Distances to and from Light-Rail Transit Stations. Retrieved on:
April 24, 2012. School of Architecture McGill University website,
http://www.arch.mcgill.ca/prof/luka/urbandesignhousing/602fall2010/read
ings/OSullivanMorrall1996.pdf
Ormsby, T., Napoleon, E., Burke, R., Groessl, C., & Bowden, L. (2010). Getting To Know ArcGIS Desktop.
ESRI Press. P 89 – 529.
Watson, T. (2005). Metropolitan Growth and Neighborhood Segregation by Income. Retrieved on: April 18,
2012. Williams College website,
http://web.williams.edu/Economics/seminars/watson_brook_1105.pdf
Wikipedia (2012). State Plane Coordinate System. Retrieved on: May 30, 2012. Wikipedia in English website,
http://en.wikipedia.org/wiki/State_Plane_Coordinate_System
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7.0. APPENDICES
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