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 COVER UNITED STATES DEPARTMENT OF THE INTERIOR U.S. GEOLOGICAL SURVEY CITY OF SAN ANTONIO'S INTERACTIVE GIS WEBSITES TEXAS NATURAL RESOURCES INFORMATION SYSTEM ESRI

Methodology Small Scale Raster Analysis

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 Population Totals Major Roads Universities (With A&M) Multiple Ring Buffer 3) Select by Attribute (Distance) Select (Hispanic Population) Multi-Ring Buffer Selection Multi-Ring Buffer of Major Roads “Best” Hispanic Population “Good” Hispanic Population Select (Hispanic Population) Block Groups with Hispanic Population Selection “Good” Major Roads Buffer “Best” Major Roads Buffer Block Groups with Hispanic Population Selection Create Layer from Selection Create Layer from Selection Create Layer from Selection Multiple Ring Buffer Select by Attribute (Distance) Multi-Ring Buffer Selection Create Layer from Selection Multi-Ring Buffer of Universities Select by Attribute (Distance = 10) Select by Attribute (Distance = 5) Select by Attribute (Distance = 2) Multi-Ring Buffer Selection Multi-Ring Buffer Selection Multi-Ring Buffer Selection Create Layer from Selection Create Layer from Selection Create Layer from Selection “Bad” Universities Buffer “Good” Universities Buffer “Best” Universities Buffer Intersect Query Selection 2 Intersect Query Selection 3 Intersect Query Selection 4 Select by Location 1 Intersect Query (Best+Good+ Best) Select by Location 2 Intersect Query (Best+Best+ Best) Intersect Query Selection 1 Select by Location 3 Intersect Query (Good+Best+ Best) Select by Location 4 Intersect Query (Best+Best+ Good) Vector Analysis

Intersect Query Selection 2 Intersect Query Selection 3 Intersect Query Selection 4 Intersect Query Selection 1 Create Layer from Selection Create Layer from Selection Create Layer from Selection Create Layer from Selection Prospect Layer 4 Prospect Layer 3 Prospect Layer 2 Prospect Layer Year Floodplain DOQQs Union Most Acceptable Criteria Clip Acceptable Outside of Floodplain Digitize Available Land 2 nd Analysis Output

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

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.