Lone Star Members

Project Manager: Bob Armentrout

Assistant Manager: Nina Castillo

Web Designer: Daniel Roberts

Analysts: Cade Colston, Mehs Ess, Linda Porter

Identifying Locations for a Future Texas State University at San Antonio with the Goal of Increasing Hispanic Enrollment

Prepared by Lone Star Spatial Solutions

Abstract

• In response to the growing Hispanic population in Texas, Lone Star Spatial Solutions has completed extensive analyses in an effort to pinpoint two potential sites for a Hispanic Serving Institution satellite campus of Texas State University in San Antonio, Texas.

• Sites are based on locations of existing universities, Hispanic population density, transportation routes, and land availability.

Introduction

• Hispanic Serving Institutions (HSI) are defined as non-profit institutions that have at least 25% Hispanic full-time equivalent (FTE) enrollment, and of the Hispanic student enrollment, at least 50% are low income

• Hispanic Serving Institutions are eligible for Title V status and receive grants from the federal government– This will increase for renovation of instructional

facilities, faculty development, etc …

• The Hispanic population in Texas is growing from 27.6 % in 1995 to an expected 37.6 % in 2025

Introduction, cont.

• San Antonio is a prime location for the creation of a Texas State satellite campus for the purpose of increasing Hispanic enrollment

• Achieving Title V institution status would not only be for the benefit of increased funding, but for the purpose of educating the future workforce of Texas, allowing it to compete with more progressive States

Data

Data Types:• County/City Boundaries• Census Block Group Boundaries• Topographically Integrated Geographic Encoding

And Referencing Systems (TIGER) files • Demographic Data, Social, Economic, and

Housing• Digital Orthophoto Quarter Quadrangle (DOQQ)• Land Use Land Cover (LULC)• Current Universities Locations

Data Sources

• WEBGIS LAND USE LAND COVERhttp://www.webgis.com/lulcdata.html

• UNITED STATES DEPARTMENT OF THE INTERIOR U.S. GEOLOGICAL SURVEYhttp://www.vterrain.org/Culture/LULC/Data_Users_Guide_4.html

• CITY OF SAN ANTONIO'S INTERACTIVE GIS WEBSITEShttp://maps.sanantonio.gov/

• TEXAS NATURAL RESOURCES INFORMATION SYSTEMhttp://www.tnris.state.tx.us

• ESRIhttp://www.esri.com/

Methodology

Small Scale

Raster Analysis

LULCRaster

UniversitiesReclass

RoadsReclass

Selected LULC

PopulationDensityReclass

PercentHispanicReclass

PopulationDensityRaster

PercentHispanicRasterBlock Groups

With Percent Hispanic andPopulation

Density

Block Groups with Race

And PopulationTotalsSF1

Tabular Data

Universities

LULC

BlockGroups

MajorRoads

Feature to Raster(Distance)

Feature to Raster(Distance)

Select by Attribute

(Code = 10s,20s, 30s)

MultipleRing

Buffer(10 @ 1mi)

MultipleRing

Buffer(10 @ 0.2mi)

Reclassify toValues 1-10

Reclassify toValues 1-10

Feature to Raster(Population Density)

Feature to Raster(Percent Hispanic)

Add Field/Calculate

Values

Join

Add Field/Calculate

Values

Feature to Raster(LULC Code)

UniversitiesBufferRaster

Universities(With A&M)

MajorRoadsBuffer

AppropriateLULC

UniversitiesBuffer

Major RoadsBuffer Raster

Add NewFeature

CreateLayer from Selection

Reclassify toValues 1-10

Reclassify toValues 1-10

Reclassify toValues 1-3

LULCReclass

Raster Analysis

Weighted Calculation 1((Percent Hispanic Reclass) * (.4)) +

((Roads Reclass) * (.3)) +((Population Density Reclass) * (.2)) +

((Universities Reclass) * (.1))

Weighted Calculation 2((Percent Hispanic Reclass) * (.5)) +

((Roads Reclass) * (.2)) +((Universities Reclass) * (.2)) +

((Population Density Reclass) * (.1))

WeightedResult 1

(Values 1.3-8.3)

WeightedResult 2

(Values 1.4-8)

Reclassify fromValues 7-8 to 1

Reclassify fromValues 7-8.3 to 1

WeightedResult 1

WeightedResult 2

Calculation 4Weighted Result 2 *

LULC Reclass

Calculation 3Weighted Result 1 *

LULC Reclass

LULCReclass

1st AnalysisOutput 1

Values 1-3

1st AnalysisOutput 2

Values 1-3UniversitiesReclass

RoadsReclass

PopulationDensityReclass

PercentHispanicReclass

LULCReclass

Layers Used in Raster Calculations

Formula (Percentage of Hispanic Population X (.4)) + (Roads X (.3)) + (Population Density X (.2)) + (Existing Universities X (.1)) = First Weighting

Formula

(Percentage of Hispanic Population) X (.4)) + (Roads X (.3)) + (Population Density X (.2)) +

(Existing Universities X (.1))

Methodology

Large Scale

Vector Analysis

Block Groups with Race

And PopulationTotals

MajorRoads

Universities(With A&M)

MultipleRing Buffer

(3 @ 3)

Select byAttribute

(Distance)

Select(Hispanic

Population)

Multi-RingBuffer

SelectionMulti-Ring Buffer of

Major Roads

“Best”Hispanic

Population

“Good”Hispanic

Population

Select(Hispanic

Population)

Block Groupswith HispanicPopulation Selection

“Good”Major Roads

Buffer

“Best”Major Roads

Buffer

Block Groupswith HispanicPopulation Selection

Create Layer from Selection

Create Layer from Selection

Create Layer from Selection

MultipleRing Buffer(10 @ .2) Select by

Attribute(Distance)

Multi-RingBuffer

Selection

Create Layer from Selection

Multi-Ring Buffer of

Universities

Select byAttribute

(Distance = 10)

Select byAttribute

(Distance = 5)

Select byAttribute

(Distance = 2)

Multi-RingBuffer

Selection

Multi-RingBuffer

Selection

Multi-RingBuffer

Selection

Create Layer from Selection

Create Layer from Selection

Create Layer from Selection

“Bad”Universities

Buffer

“Good”Universities

Buffer

“Best”Universities

Buffer

IntersectQuery

Selection 2

IntersectQuery

Selection 3

IntersectQuery

Selection 4

Select byLocation 1

Intersect Query(Best+Good+

Best)

Select byLocation 2

Intersect Query(Best+Best+

Best)

IntersectQuery

Selection 1

Select byLocation 3

Intersect Query(Good+Best+

Best)

Select byLocation 4

Intersect Query(Best+Best+

Good)

Vector Analysis

IntersectQuery

Selection 2

IntersectQuery

Selection 3

IntersectQuery

Selection 4

IntersectQuery

Selection 1

Create Layer from Selection

Create Layer from Selection

Create Layer from Selection

Create Layer from Selection

ProspectLayer 4

ProspectLayer 3

ProspectLayer 2

ProspectLayer 1

100-YearFloodplain

DOQQs

UnionMost

AcceptableCriteria

ClipAcceptableOutside ofFloodplain

DigitizeAvailable Land

2nd AnalysisOutput

Results

Discussion• Raster analysis can display inaccurate spatial references

since the cell size determines the resolution. • The vector-raster conversion can pose data integrity

problems due to generalization and choice of inappropriate cell size.

• Most raster outputs do not possess high-quality cartographic needs.

• Vector analysis may be more aesthetically pleasing, however any type of filtering through spatial analysis is impossible to do within polygons.

• Digitized areas could possibly be inaccurate due to human error.

• There are other suitable areas near the selected locations. However, they have fallen outside of the study area due to the criteria chosen for this project. All areas of consideration will require further research for land availability

Final Deliverables

• Detailed/Comprehensive Final Report• Printed Map• Lone Star Spatial Solutions website

and related links• DVD+R containing all organized data,

metadata, final report, finalized maps, and slide show of final presentation

http://geosites.evans.txstate.edu/~g4427f05-02/

Conclusion

• The areas determined are suitable for a satellite campus in San Antonio.

• A satellite campus in these areas has the greatest chance in becoming a Title V institution.

Thank You for Your Time.