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Land Use/Land Cover Planning Nexus: a Space-Time Multi-Scalar Assessment of Urban Growth in the Tulsa Metropolitan Statistical Area

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Abstract

This study employs remote sensing, geographic information systems (GIS), and spatial statistical modeling to structurally characterize urban growth and spatially understand its drivers in an effort to assess the outcome of the 1974 Tulsa Metropolitan Statistical Area (TMSA) comprehensive land use plan. Results demonstrate that the TMSA witnessed significant alterations in land use/land cover (LULC) spatial extent and structure over the assessment period and further illustrate that median household income, population density, sales, and construction cost are key drivers that influenced the structural character of LULC between 1990 and 2011. The assessment shows that the spatial and temporal patterns within development districts deviated from that prescribed in the comprehensive plan while spatial development within intensity corridors mirrors the goals and objectives set in the plan. Aberrations between plan objectives and outcomes can be attributed to upward mobility in financial status, growth in markets, and political climate.

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References

  • Aguilera F., Valenzuela L. M., and Botequilha-Leitão A. (2011). Landscape metrics in the analysis of urban land use pattern: a case study in a Spanish metropolitan area. Landscape and Urban Planning 99: 226–238.

    Article  Google Scholar 

  • Alterman R., and Hill M. (1978). Implementation of urban land use plans. Journal of the American Institute of Planners 44(3): 274–285.

    Article  Google Scholar 

  • Baer W. (1997). General plan evaluation criteria: an approach to making better plans. Journal of the American Planning Association 63(3): 329–344.

    Article  Google Scholar 

  • Bengston D. N., Fletcher J. O., and Nelson K. C. (2004). Public policies for managing urban growth and protecting open space: policy instruments and lessons learned in the United States. Landscape and Urban Planning 69(2–3): 271–286.

    Article  Google Scholar 

  • Berke P. R., and Conroy M. M. (2000). Are we planning for sustainable development? An evaluation of 30 comprehensive plans. Journal of the American Planning Association 66(1): 21–33.

    Article  Google Scholar 

  • Berke P., Backhurst M., Laurian L., Day M., Crawford J., Ericksen N., and Dixon J. (2006). What makes plan implementation successful? An evaluation of local plans and implementation practices in New Zealand. Environment and Planning B: Planning and Design 33(4): 581–600.

    Article  Google Scholar 

  • Blaschke T. (2010). Object based image analysis for remote sensing. ISPRS Journal of Photogrammetry & Remote Sensing 65: 2–16.

    Article  Google Scholar 

  • Brody S. D., and Highfield W. E. (2005). Does planning work? Testing the implementation of local environmental planning in Florida. Journal of the American Planning Association 71(2): 159–175.

    Article  Google Scholar 

  • Center for Economic and Management Research (2002). Oklahoma. Business Bulletin 70(4): 1–25.

    Google Scholar 

  • Chapin T. S., and Deyle R. E. (2008). A parcel-based GIS method for evaluating conformance of local land-use planning with a state mandate to reduce exposure to hurricane flooding. Environment and Planning B: Planning & Design 35(2): 261–279.

    Article  Google Scholar 

  • Chasco, C., Garcia, I., and Vicens, J. (2007). Modelling spatial variations in household disposable income with geographically weighted regression, Munich personal RePEc archive (MPRA), Working Paper, No. 1682.

  • Congalton R. G. (1991). A review of assessing the accuracy of classification of remotely sensed data. Remote Sensing of the Environment 37: 35–46.

    Article  Google Scholar 

  • Dalton L. (1989). The limits of regulation: evidence from local plan implementation in California. Journal of the American Planning Association 55(2): 151–168.

    Article  Google Scholar 

  • DeFries R. S., Rudel T., Uriarte M., and Hansen M. (2010). Deforestation driven by urban population growth and agricultural trade in the twenty-first century. Nature Geoscience 3: 178–181.

    Article  Google Scholar 

  • Deng X., Huang J., Rozelle S., and Uchida E. (2008). Growth, population and industrialization, and urban land expansion of China. Journal of Urban Economics 63(1): 96–115.

    Article  Google Scholar 

  • Eastman, J.R. (2015). TerrSet Manual. Accessed in TerrSet version 18.03. Clark University, Worcester, MA: pages 1–390.

  • Faludi A. (1987). A decision-Centred view of environmental planning, Pergamon Press, Oxford.

    Google Scholar 

  • Foresman T. W., Pickett S. T., and Zipperer W. C. (1997). Methods for spatial and temporal land use and land cover assessment for urban ecosystems and application in the greater Baltimore-Chesapeake region. Urban Ecosystems 1: 201–216.

    Article  Google Scholar 

  • Fotheringham A. S., Brunsdon C., and Charlton M. (2002). Geographically weighted regression: the analysis of spatially varying relationships, John Wiley, West Sussex.

    Google Scholar 

  • Gennaio M., Hersperger A. M., and Burgi M. (2009). Containing urban sprawl– evaluating effectiveness of urban growth boundaries set by the Swiss land use plan. Land Use Policy 26: 224–232.

    Article  Google Scholar 

  • Global Insights. (2012). US Metro Economies 2012 Outlook –Gross Metropolitan Product, and Critical Role of Transport Infrastructure. Retrieved from http://usmayors.org/metroeconomies/0712/FullReport.pdf

  • Google Earth. (2014). Google Earth Pro (Version 6.1.0.5001) [Software]. Retrieved January 11, 2014 Mountain view, CA: Google Earth.

  • Herold M., Scepan J., and Clarke K. C. (2002). The use of remote sensing and landscape metrics to describe structures and changes in urban land uses. Environment and Planning A 34: 1443–1458.

    Article  Google Scholar 

  • Hoagland B. (2000). The vegetation of Oklahoma: a classification for landscape mapping and conservation planning. The Southwestern Naturalist 45(4): 385–420.

    Article  Google Scholar 

  • Irwin E. G., and Geoghegan J. (2001). Theory, data, methods: developing spatially explicit economic models of land use change. Agriculture, Ecosystems and Environment 85: 7–23.

    Article  Google Scholar 

  • Iverson L. R., and Cook E. A. (2000). Urban forest cover of the Chicago region and its relation to household density and income. Urban Ecosystems 4: 105–124.

    Article  Google Scholar 

  • Jantz C. A., Goetz S. J., and Shelley M. K. (2003). Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environment and Planning B: Planning and Design 30: 251–271.

    Google Scholar 

  • Jenerette G. D., and Wu. J. (2001). Analysis and simulation of land-use change in the Central Arizona-Phoenix region, USA. Landscape Ecology 16: 611–626.

    Article  Google Scholar 

  • Kahya O., Bayram B., and Reis S. (2010). Land cover classification with an expert system approach using Landsat ETM imagery: a case study of Trabzon. Environmental Monitoring and Assessment 160: 431–438.

    Article  Google Scholar 

  • Kaiser E., Godschalk D., and Chapin F. (1995). Urban land use planning (4th edition), University of Illinois Press, Urbana, IL.

    Google Scholar 

  • Kline J. D., and Alig R. J. (1999). Does land use planning slow the conversion of forest and farmlands? Growth and Change 30: 3–22.

    Article  Google Scholar 

  • Lambin, E.F., Turner, B.L., Geist, H.J., Agbola, S.B., et al. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change, 11, 261–269.

  • Laurian L., Day M., Backhurst M., Berke P., Ericksen N., Crawford J., Dixon J., and Chapman S. (2004). What drives plan implementation? Plans, planning agencies and developers. Journal of Environmental Planning and Management 47(4): 555–577.

    Article  Google Scholar 

  • Laurian L., Crawford J., Day M., Kouwenhoven P., Mason G., Ericksen N., and Beattie L. (2010). Evaluating the outcome of plans: theory, practice, and methodology. Environment and Planning B: Planning and Design 37: 740–757.

    Article  Google Scholar 

  • Lo C. P., and Yang X. (2002). Drivers of land-use/land-cover changes and dynamic modelling for the Atlanta, Georgia metropolitan area. Photogrammetric Engineering and Remote Sensing 68(10): 1073–1082.

    Google Scholar 

  • Long H., Wu X., Wang W., and Dong G. (2008). Analysis of urban-rural land-use change during 1995-2006 and its policy dimensional driving forces in Chongqing, China. Sensors 8: 681–699.

    Article  Google Scholar 

  • Lu D., Mausel P., Brondizio E., and Moran E. (2004). Change detection techniques. International Journal of Remote Sensing 25(12): 2365–2407.

    Article  Google Scholar 

  • Mastop H., and Faludi A. (1997). Evaluation of strategic plans: the performance principle. Environment and Planning B: Planning and Design 24(6): 815–832.

    Article  Google Scholar 

  • McKinnish T., Walsh R., and White T. K. (2010). Who gentrifies low-income neighborhoods? Journal of Urban Economics 67(2): 180–193.

    Article  Google Scholar 

  • Miller R., and Small C. (2003). Cities from space: potential applications of remote sensing in urban environmental research and policy. Environmental Science & Policy 6(2): 129–137.

    Article  Google Scholar 

  • Minnesota Population Center. (2011). National Historical Geographic Information System: version 2.0. Minneapolis, MN: University of Minnesota. Retrieved February 20, 2014 from - http://www.nhgis.org

  • Nakaya, T., Charlton, M., Lewis, P., Brunsdon, C., Yao, J., and Fotheringham, S. (2014). GWR 4 manual. Retrieved January 16, 2015 from - https://geodacenter.asu.edu/drupal_files/gwr/GWR4manual.pdf

  • National Climate Data Center. (2014). Land-based station data. Retrieved December 11, 2014 from - http://www.ncdc.noaa.gov/

  • Nelson A., and Moore T. (1996). Assessing growth management policy implementation: case study of the United States’ leading growth management state. Land Use Policy 13(4): 241–259.

    Article  Google Scholar 

  • Oklahoma Integrated Information Network. (2015). Oklahoma economic indicators databases. Accessed 2 June 2015.

  • Oliveira V., and Pinho P. (2010). Evaluation in urban planning: advances and prospects. Journal of Planning Literature 24(4): 343–361.

    Article  Google Scholar 

  • Pauleit S., and Duhme F. (2000). Assessing the environmental performance of land cover types for urban planning. Landscape and Urban Planning 52(1): 1–20.

    Article  Google Scholar 

  • Price, C.P., Nakagaki, N., Hitt, K.J., and Clawges, R.M. (2006). Enhanced historical land-use and land-cover datasets of the U.S. Geological Survey. U.S. Geological Survey Data Series 240, digital maps, accessed on 8 July 2014 at http://pubs.usgs.gov/ds/2006/240

  • Rounsevell M., Reginster I., Araujo M., Carter T., Dendoncker N., and Ewert F., et al. (2006). A coherent set of future land use change scenarios for Europe. Agriculture, Ecosystems & Environment 114(1): 57–68.

  • Schmidt, G.L., Jenkerson, C.B., Masek, J., Vermote, E., and Gao, F. (2013). Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description: U.S. Geological Survey Open-File Report 2013–1057, 17 p.

  • Schneider A., and Woodcock C. (2008). Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Studies 45(3): 659–692.

    Article  Google Scholar 

  • Seto K. C., and Kaufmann R. K. (2003). Modeling the drivers of urban land use change in the Pearl River Delta, China: integrating remote sensing with socioeconomic data. Land Economics 79(1): 106–121.

    Article  Google Scholar 

  • Seto K. C., Fragkias M., Guneralp B., and Reilly M. K. (2011). A meta-analysis of global urban land expansion. PLoS ONE 6(8): e23777. doi:10.1371/journal.pone.0023777.

    Article  Google Scholar 

  • Snyder B., and Coglianese C. (2005). Measuring progress: program evaluation of environmental policies. Environment 47(2): 22–39.

    Article  Google Scholar 

  • Talen E., and Anselin L. (1998). Assessing spatial equity: an evaluation of measures of accessibility to public playgrounds. Environment and Planning A 30(4): 595–613.

    Article  Google Scholar 

  • Tan M., Li X., Xie H., and Lu C. (2005). Urban land expansion and arable land loss in China–a case study of Beijing-Tianjin-Hebei region. Land Use Policy 22: 187–196.

    Article  Google Scholar 

  • Thapa R., and Murayama Y. (2010). Drivers of urban growth in the Kathmandu Valley, Nepal: examining the efficiency of the analytic hierarchy process. Applied Geography 30(1): 70–83.

    Article  Google Scholar 

  • Toutin T. (2004). Review article: geometric processing of remote sensing images: models, algorithms and methods. International Journal of Remote Sensing 25(10): 1893–1924.

    Article  Google Scholar 

  • Tulsa Metropolitan Area Planning Commission (1974). Vision 2000: phase two summary: urban form selection, implementation policies, development guidelines, Tulsa, OK.

    Google Scholar 

  • USDA [United States Department of Agriculture]. (2014). National Agriculture Imagery Program. Retrieved 15 Jan 2014 from - http://nrcs.usda.gov

  • USGS [United States Geological Survey]. (2014). Earth Resources Observation and Science Centre. Satellite image collections. Retrieved 4 Jan 2014 from - http://glovis.usgs.gov

  • Veldkamp A., and Fresco L. O. (1997). Reconstructing land use drivers and their spatial scale dependence for Costa Rica (1973 and 1984). Agricultural Systems 55(1): 19–43.

    Article  Google Scholar 

  • Veldkamp A., and Verburg P. H. (2004). Modelling land use change and environmental impact. Journal of Environmental Management 72(1–2): 1–3.

    Article  Google Scholar 

  • Verburg P. H., Schot P. P., Dijst M. J., and Veldkamp A. (2004). Land use change modelling: current practices and research priorities. GeoJournal 61: 309–324.

    Article  Google Scholar 

  • Wang Y., and Moskovits D. K. (2001). Tracking fragmentation of natural communities and changes in land cover: applications of Landsat data for conservation in an urban landscape (Chicago wilderness). Conservation Biology 15(4): 835–843.

    Article  Google Scholar 

  • Wassmer R. W. (2006). The influence of local urban containment policies and statewide growth management on the size of United States urban areas. Journal of Regional Science 46(1): 25–65.

    Article  Google Scholar 

  • Wilson C. (2014). Spectral analysis of civil conflict-induced forced migration on land-use/land cover change: the case of a primate and lower-ranked cities in Sierra Leone. International Journal of Remote Sensing 35(3): 1094–1125.

    Article  Google Scholar 

  • Wilson C. O. (2015). Land use/land cover water quality nexus: quantifying anthropogenic influences on surface water quality. Environmental Monitoring and Assessment 187: 424. doi:10.1007/s10661-015-4666-4.

    Article  Google Scholar 

  • Wilson C. O., and Weng Q. (2011). Simulating the impacts of future land use and climate changes on surface water quality in the Des Plaines River watershed, Chicago metropolitan statistical area, Illinois. Science of the Total Environment 409(20): 4387–4405.

    Article  Google Scholar 

  • Wilson S. A., and Wilson C. O. (2013). Modelling the impacts of civil war on land use and land cover change within Kono District, Sierra Leone: a socio-geospatial approach. Geocarto International 28(6): 476–501.

    Article  Google Scholar 

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Acknowledgments

This research is sponsored by funds from the Department of History and Political Science, Rogers State University. The authors wishes to thank several agencies including but not limited to Tulsa Metropolitan Area Planning Commission, several county planning offices in the Tulsa Metropolitan Area, and the GWR 4 Development Team for data, software, and other supporting materials. The authors would also like to express gratitude to two anonymous individuals, manuscript reviewers, and all those who gave positive feedback on the research at conference presentations that helped improve the manuscript.

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Authors and Affiliations

  1. Department of History and Political Science, Rogers State University, 1703 W. Will Rogers Blvd, Claremore, OK, 74017, USA

    Sigismond A. Wilson

  2. Department of Geography & Anthropology, University of Wisconsin Eau Claire, 105 Garfield Avenue, Eau Claire, WI, 54702, USA

    Cyril O. Wilson

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  1. Sigismond A. Wilson
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Correspondence to Cyril O. Wilson.

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This study was funded by the Department of History and Political Science, Rogers State University.

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Wilson, S.A., Wilson, C.O. Land Use/Land Cover Planning Nexus: a Space-Time Multi-Scalar Assessment of Urban Growth in the Tulsa Metropolitan Statistical Area. Hum Ecol 44, 731–750 (2016). https://doi.org/10.1007/s10745-016-9857-2

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