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A Spatial Socio-Demographic Analysis around Texas National Parks

Kyunghee Lee and Dr. Michael A. Schuett The Center for Socioeconomic Research & Education,

Department of Recreation, Park and Tourism Sciences, Texas A&M University

Method Abstract

Introduction

Discussion & Conclusion

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  Working with Community - The National Park Service (NPS) manages nearly 400 park units…and works in almost every one of 3,141counties in the U.S. (cited in http://www.nps.gov/communities/index.htm).

 The Disparity in National Park Visitation - Racial/ethnic minorities are largely absent among visitors to national parks (Goldsmith 1994). -Non-Hispanic whites comprised 78% of park visitors in 2008–2009. By comparison, Hispanics accounted for 9% of visitors (The National Park System Comprehensive Survey, 2011).

 Future is Now - United States: from 281 million in 2000 to 308.7 million - Texas: 25.1 million (2010 Census). - Hispanic or Latino (of any race) population now makes up 37.6% of the TX population. - In Texas, the population characterized as “minority” will become the majority population by 2020 (Murdock et al. 1997).

 Park Planning & Management - “Managers will need to meet the needs of new markets that may be unfamiliar with the local treasure or their protected area designation” (Schuett & Hollenhorst, 2010, p. 208). - Managers/planners need to become more aware of what social changes are taking place outside park boundaries.

Purpose of this Study  Showing Cluster Patterns around National Park units -This study illustrates how the area-based spatial cluster analysis method was used to detect socio-demographic patterns around National Parks in Texas.  Analyzing Temporal Spatial Patterns - Identifies the patterns/overall trends of data. - Are features clustered; what was the overall pattern from 1990-2010? - Detecting spatial autocorrelation.  Mapping Clusters - Local calculations. - Identifies the extent and location of clustering or dispersion. - Where are the clusters (or where are the hot spots)?

Results

Spatial Cluster Analysis   Definition: Spatial cluster analysis detects nonrandomness of spatial patterns or existence of spatial autocorrelation.

  Geoda (software): This study used spatial cluster analysis with GEODA software to analyze what kind of similarities and dissimilarities exist within NPS proximate counties.  Spatial Autocorrelation - Correlation of a variable with itself through space - Most statistics are based on the assumption that the values of observations in each sample are independent of one another.

The purpose of this research is to detect socio-demographic changes around National Park units in Texas. Socio-demographic variables are important indicators in predicting future trends and provide beneficial information about potential park visitors for park managers and planners. Specifically, specific racial/ethnic minority populations have increased in many areas of the state. Understanding this trend is relevant for future planning of our National Parks; however, there are few studies that have examined this type of change regionally. The result of this analysis shows clustered areas and key trends based on ethnic/racial variables within park proximate counties; management implications and future research are discussed.

  Moran’s I - One of the oldest indicators of spatial autocorrelation (Moran, 1950).

"   The Results show the clustered areas and what kind of spatial autocorrelations exist among different ethnic/racial groups: Trends have shown that Hispanic populations have dramatically increased in Texas but the cluster areas (hot spots) were not known

 Counties around National Park areas have similar socio-demographic patterns over the time periods  Moran’s I statistics were positive and significant (p-value = 0.05 or 0.01) in each distribution.   During the period of study (1990–2010), we found a general trend of increasing spatial autocorrelation in each distribution.

 Residential segregation exists around different NPS areas   The Hispanic population has been mostly concentrated in South and East Texas, whereas the African American population has been focused in west Texas. Furthermore, the White population has been concentrated in central and north Texas.   Interestingly, the percent of the Hispanic population has increased on the east and north parts of the state.   Results of the spatial autocorrelation tests between the Hispanic and White populations showed negative values (Moran’s I = -0.6978).   Hispanic and African American populations showed negative values (Moran’s I = -0.4197).

  This study used ethnic/racial populations to detect the socio- demographic patterns of county residents around national park areas. Further research should use more variables such as age, income, education, population density, etc.   This research only examined 12 National Parks in Texas. Future research should examine other types of park units, e.g., Recreation Areas, Historical Parks (see Schuett & Hollenhorst, 2010).   Geographically, we only analyzed parks in TX; other states and adjacent counties should be examined as well. References Anselin, L. (1995) Local indicators of spatial association: LISA, Geographical Analysis, 27, pp. 93–115. Anselin,L. (2003) GeoDa0.9 User’s Guide (Urbana-Champaign, IL: University of Illinois, Spatial Analysis Laboratory). Patricia A. Taylor, Burke D. Grandjean, and James H. Gramann (2011). National Park Service Comprehensive Survey of the American Public 2008–2009, National Park Service, Washington DC. Goldsmith, J. (1994). Designing for diversity. National Parks 68 (May/June), 20-21. Murdock, S.H., M.D. Hoque, M. Michael, S. White, and B. Pecotte. (1997). The Texas Challenge: Population Change and the Future of Texas. College Station: Texas A&M University Press. Schuett, M. A., Le, L., & Hollenhorst, S. J. (2010). Who visits the US national parks? An analysis of park visitors and visitation: 1990-2008. World Leisure Journal, 52(3), 200-210. Tobler, W. (1979) Cellular geography, In: S. Gale & G. Olsson (Eds). Philosophy in Geography, pp. 379–386.

  Thinking Regionally, Acting Locally ! - Spatial pattern analysis shows a starting point to prepare for enhanced civic engagement and visitor management/ planning. - From a marketing perspective, park managers could prepare targeted plans for attracting locals (potential visitors) who may not know what is available at certain parks, (e.g., public service ads in Spanish). This approach is especially true for more urban park units.

 Further Research

  Goals of Spatial Autocorrelation – Measure the strength of spatial autocorrelation in a map – Tests the assumption of independence or randomness.

•  We created a 100 mile radius from 12 National Parks as a centroid to capture which counties belong to NPS areas and the effect

of demographic change in those areas. •  Data were examined using the 1990-2000 and 2000-2010 census from counties around these NPS areas.

- Negative (positive) values indicate negative (positive) spatial autocorrelation. Values range from −1 (perfect dispersion) to +1 (perfect correlation). A zero value indicates a random spatial pattern.

  Civic engagement is important for NPS management - Civic engagement involves building and sustaining relationships with neighbors and other communities of interest, both near and far (www.nps.gov/communities). - Our results stress the issue of “who” lives in counties near park unit areas.

  Population changes proximate to National Parks is a trend worth tracking and monitoring - According to Schuett & Hollenhorst (2010), 25.7% of park visitors visited in National parks living within 100 miles. - Specifically, 52.4% of respondents who live within 100 miles from urban recreation/shoreline area had visited park units. - In this context, we may predict what groups could be potential visitors or volunteers to specific parks units.

Funding for this project was provided by National Park Service