lone star members project manager: bob armentrout assistant manager: nina castillo web designer:...
TRANSCRIPT
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.