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Discovery and Fitness for Use

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Geographic Information

Part of the book series: Springer Geography ((SPRINGERGEOGR))

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Abstract

The purpose of this chapter is to provide readers with a succinct, but varied list of resources for obtaining geographic information (GI) for use, analyses, and geovisualization. We necessarily limit this list to authoritative outlets, such as those from federal, state, and local organizations, as well as private data vendors that are actively engaged in secondary data markets. To facilitate description, data types are subdivided into the 16 National Geospatial Data Asset (NGDA) themes identified by the Federal Geographic Data Committee (FGDC). Fitness for use, as a concept, is detailed and an example is provided using telecommunications data.

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References

  • Ames, D. P., Horsburgh, J. S., Cao, Y., Kadlec, J., Whiteaker, T., & Valentine, D. (2012). HydroDesktop: Web services-based software for hydrologic data discovery, download, visualization, and analysis. Environmental Modelling & Software, 37, 146–156. doi:10.1016/j.envsoft.2012.03.013.

    Article  Google Scholar 

  • Bader, M. D., Ailshire, J. A., Morenoff, J. D., & House, J. S. (2010). Measurement of the local food environment: A comparison of existing data sources. American Journal of Epidemiology, 171(5), 609–617. doi:10.1093/aje/kwp419.

    Article  Google Scholar 

  • Bailey, R. G., U.S. Fish and Wildlife Service, & United States (1980). Description of the ecoregions of the United States. Washington, DC: U.S. Dept. of Agriculture, Forest Service.

    Google Scholar 

  • Belobaba, P. P. (1987). Survey paper-airline yield management an overview of seat inventory control. Transportation Science, 21(2), 63–73. doi:10.1287/trsc.21.2.63.

    Article  Google Scholar 

  • Bishop, B. W. & Hank, C. F. (2016). Data curation profiling of biocollections. American Society for Information Science and Technology Annual Meeting, Copenhagen, Denmark.

    Google Scholar 

  • Bishop, B. W., Grubesic, T. H., & Prasertong, S. (2013). Digital curation and the GeoWeb: An emerging role for geographic information librarians. Journal of Map & Geography Libraries, 9(3), 296–312. doi:10.1080/15420353.2013.817367.

    Article  Google Scholar 

  • Boone, J. E., Gordon-Larsen, P., Stewart, J. D., & Popkin, B. M. (2008). Validation of a GIS facilities database: Quantification and implications of error. Annals of Epidemiology, 18(5), 371–377. doi:10.1016/j.annepidem.2007.11.008.

    Article  Google Scholar 

  • de Bruin, S. D., Bregt, A., & Ven, M. V. D. (2001). Assessing fitness for use: The expected value of spatial data sets. International Journal of Geographical Information Science, 15(5), 457–471. doi:10.1080/13658810110053116.

    Article  Google Scholar 

  • Campbell, J. P., Gridley, T. S., & Muelleman, R. L. (1997). Measuring response intervals in a system with a 911 primary and an emergency medical services secondary public safety answering point. Annals of Emergency Medicine, 29(4), 492–496. doi:10.1016/S0196-0644(97)70222-1.

    Article  Google Scholar 

  • Cayo, M. R., & Talbot, T. O. (2003). Positional error in automated geocoding of residential addresses. International Journal of Health Geographics, 2(1), 1–12. doi:10.1186/1476-072x-2-10.

    Article  Google Scholar 

  • Chrisman, N. R. (1984). Part 2: Issues and problems relating to cartographic data use, exchange and transfer: The role of quality information in the long-term functioning of a geographic information system. Cartographica: The International Journal for Geographic Information and Geovisualization, 21(2–3), 79–88. doi:10.3138/7146-4332-6J78-0671.

    Article  Google Scholar 

  • Devillers, R., Bédard, Y., & Jeansoulin, R. (2005). Multidimensional management of geospatial data quality information for its dynamic use within GIS. Photogrammetric Engineering & Remote Sensing, 71(2), 205–215. doi:10.14358/PERS.71.2.205

    Google Scholar 

  • Devillers, R., Bédard, Y., Jeansoulin, R., & Moulin, B. (2007). Towards spatial data quality information analysis tools for experts assessing the fitness for use of spatial data. International Journal of Geographical Information Science, 21(3), 261–282. doi:10.1080/13658810600911879.

    Article  Google Scholar 

  • Devillers, R., Stein, A., Bédard, Y., Chrisman, N., Fisher, P., & Shi, W. (2010). Thirty years of research on spatial data quality: Achievements, failures, and opportunities. Transactions in GIS, 14(4), 387–400. doi:10.1111/j.1467-9671.2010.01212.x.

    Article  Google Scholar 

  • Elliott, A. (2014). Top 10 Broadband Carriers Compete: DSL Kicking Tail. IDinsight. Retrieved from http://idinsight.com/featured/top-10-broadband-carriers-compete-dsl-kicking-tail/

  • ESRI. (2015). U.S. Only Datasets. Retrieved from http://www.esri.com/data/esri_data/what-you-get

  • ESRI. (2016). ESRI Business Analyst. Retrieved from http://www.esri.com/software/businessanalyst

  • Fayyad, U. M., Piatetsky-Shapiro, G., Smyth, P., & Uthurusamy, R. (1996). Advances in knowledge discovery and data mining. Cambridge, MA: MIT Press.

    Google Scholar 

  • FGDC [Federal Geographic Data Committee]. 1994. National Geospatial Data Asset Management Plan. URL: https://www.fgdc.gov/policyandplanning/a-16/ngda-management-plan.

    Google Scholar 

  • Goldberg, D. W. (2011). Advances in geocoding research and practice. Transactions in GIS, 15(6), 727–733. doi:10.1111/j.1467-9671.2011.01298.x.

    Article  Google Scholar 

  • Google. (2016). Google Maps/Google Earth APIs Terms of Service. Retrieved from https://developers.google.com/maps/terms

  • Graves, W., & Gerney, B. (2016). Notes on the inconsistency of subcounty scale demographic data: A comparison of five data vendors. Papers in Applied Geography, 2(1), 121–128. doi:10.1080/23754931.2015.1086413.

    Article  Google Scholar 

  • Grubesic, T. H. (2012a). The US National Broadband Map: Data limitations and implications. Telecommunications Policy, 36(2), 113–126.

    Article  Google Scholar 

  • Grubesic, T. H. (2012b). The wireless abyss: Deconstructing the US national broadband map. Government Information Quarterly, 29(4), 532–542.

    Article  Google Scholar 

  • Grubesic, T. H., & Mack, E. A. (2015). Broadband telecommunications and regional development. New York: Routledge.

    Google Scholar 

  • Grubesic, T. H., & Murray, A. T. (2005). Geographies of imperfection in telecommunication analysis. Telecommunications Policy, 29(1), 69–94. doi:10.1016/j.telpol.2004.08.001.

    Article  Google Scholar 

  • Grubesic, T. H., Matisziw, T. C., & Murray, A. T. (2011). Market coverage and service quality in digital subscriber lines infrastructure planning. International Regional Science Review, 34(3), 368–390. doi:10.1177/0160017610386479.

    Article  Google Scholar 

  • Grubesic, T. H., Wei, R., & Murray, A. T. (2014). Spatial clustering overview and comparison: Accuracy, sensitivity, and computational expense. Annals of the Association of American Geographers, 104(6), 1134–1156.

    Article  Google Scholar 

  • Guptill, S. C., Morrison, J. L., & International Cartographic Association (1995). Elements of spatial data quality. Oxford, England: Elsevier Science.

    Google Scholar 

  • Heipke, C. (2010). Crowdsourcing geospatial data. ISPRS Journal of Photogrammetry and Remote Sensing, 65(6), 550–557. doi:10.1016/j.isprsjprs.2010.06.005.

    Article  Google Scholar 

  • Hoehner, C. M., & Schootman, M. (2010). Concordance of commercial data sources for neighborhood-effects studies. Journal of Urban Health, 87(4), 713–725. doi:10.1007/s11524-010-9458-0.

    Article  Google Scholar 

  • InfoUSA. (2016). Targeted Business Data. Retrieved from https://www.infousa.com/product/business-lists/

  • Janssen, M., Charalabidis, Y., & Zuiderwijk, A. (2012). Benefits, adoption barriers and myths of open data and open government. Information Systems Management, 29(4), 258–268. doi:10.1080/10580530.2012.716740.

    Article  Google Scholar 

  • Jayakar, K., & Park, E. A. (2013). Broadband availability and employment: An analysis of county-level data from the National Broadband Map. Journal of Information Policy, 3, 181–200. doi:10.5325/jinfopoli.3.2013.0181.

    Article  Google Scholar 

  • Kashyap, V., & Sheth, A. (1997). Semantic heterogeneity in global information systems. In M. Papazoglou & G. Schlageter (Eds.), Cooperative information systems: Current trends & directions (pp. 1–24). Sand Diego, CA: Academic.

    Google Scholar 

  • Kaufman, T. K., Sheehan, D. M., Rundle, A., Neckerman, K. M., Bader, M. D., Jack, D., & Lovasi, G. S. (2015). Measuring health-relevant businesses over 21 years: Refining the National Establishment Time-Series (NETS), a dynamic longitudinal data set. BMC Research Notes, 8(1), 507. doi:10.1186/s13104-015-1482-4.

    Article  Google Scholar 

  • Kerski, J. J., & Clark, J. (2012). The GIS guide to public domain data. Redlands, CA: ESRI Press.

    Google Scholar 

  • Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. London: Sage.

    Book  Google Scholar 

  • Klein, G. (1999). Sources of power—How people make decisions. Cambridge, MA: MIT Press.

    Google Scholar 

  • Koontz, C. M., & Jue, D. K. (2006). Public library facility closure: How research can better facilitate proactive management. Public Library Quarterly, 25(1–2), 43–56. doi:10.1300/J118v25n01_04.

    Article  Google Scholar 

  • Kruger, L. G. (2009). Broadband infrastructure programs in the American Recovery and Reinvestment Act (CRS Report No. R40436). Washington, DC: Congressional Research Service. Retrieved from https://www.acuta.org/acuta/legreg/l57.pdf

  • LaRose, R., Bauer, J. M., DeMaagd, K., Chew, H. E., Ma, W., & Jung, Y. (2014). Public broadband investment priorities in the United States: An analysis of the broadband technology opportunities program. Government Information Quarterly, 31(1), 53–64. doi:10.1016/j.giq.2012.11.004.

    Article  Google Scholar 

  • Lewis, K., Kaufman, J., Gonzalez, M., Wimmer, A., & Christakis, N. (2008). Tastes, ties, and time: A new social network dataset using Facebook.com. Social Networks, 30(4), 330–342. doi:10.1016/j.socnet.2008.07.002.

    Article  Google Scholar 

  • Madnick, S. (1999). Metadata Jones and the tower of Babel: The challenge of large-scale semantic heterogeneity. In Proceedings of the 1999 IEEE Meta-Data Conference, 1(13).

    Google Scholar 

  • Miller, H. J., & Han, J. (2009). Geographic data mining and knowledge discovery. Boca Raton, FL: CRC.

    Google Scholar 

  • Morrison, J. L. (1995). Spatial data quality. In S. C. Guptill & J. L. Morrison (Eds.), Elements of spatial data quality (pp. 1–12). New York: Elsevier Science.

    Chapter  Google Scholar 

  • Murray, A. T., Grubesic, T. H., Wei, R., & Mack, E. A. (2011). A hybrid geocoding methodology for spatio-temporal data. Transactions in GIS, 15(6), 795–809. doi:10.1111/j.1467-9671.2011.01289.x.

    Article  Google Scholar 

  • Omernik, J. M. (1987). Ecoregions of the conterminous United States. Annals of the Association of American geographers, 77(1), 118–125. doi:10.1111/j.1467-8306.1987.tb00149.x.

    Article  Google Scholar 

  • Sherali, H. D., Lee, Y., & Park, T. (2000). New modeling approaches for the design of local access transport area networks. European Journal of Operational Research, 127(1), 94–108. doi:10.1016/S0377-2217(99)00325-2.

    Article  Google Scholar 

  • Sui, D., Elwood, S., & Goodchild, M. (2013). Crowdsourcing geographic knowledge: Volunteered geographic information (VGI) in theory and practice. New York: Springer.

    Book  Google Scholar 

  • U. S. Office of Management and Budget. (2010). Coordination of geographic information and related spatial data activities. Retrieved from https://www.whitehouse.gov/sites/default/files/omb/memoranda/2011/m11-03.pdf

  • U.S. Census Bureau. (2015). TIGER/Line Shapefiles and Tiger/Line Files. Retrieved from https://www.census.gov/geo/maps-data/data/tiger-line.html

  • Veregin, H. (1999). Data quality parameters. In P. A. Longley, M. F. Goodchild, D. J. Maguire, & D. W. Rhind. (Eds.), Geographical information systems—Volume I, Principles and technical issues. (2nd ed., pp. 177–189). New York: Wiley.

    Google Scholar 

  • Walker, B. (2015). Everyday dig data statistics—2.5 quintillion bytes of data created daily. V & C. Retrieved from http://tinyurl.com/zhl9het

  • Walls, D. W. (2007). National Establishment Time-Series (Nets) Database: Coffee Analysis. SSRN 1022333. doi:10.2139/ssrn.1022333

  • Wang, X., Yu, C., & Wei, Y. (2012). Social media peer communication and impacts on purchase intentions: A consumer socialization framework. Journal of Interactive Marketing, 26(4), 198–208. doi:10.1016/j.intmar.2011.11.004.

    Article  Google Scholar 

  • Whitacre, B., Strover, S., & Gallardo, R. (2015). How much does broadband infrastructure matter? Decomposing the metro–non-metro adoption gap with the help of the National Broadband Map. Government Information Quarterly, 32(3), 261–269. doi:10.1016/j.giq.2015.03.002.

    Article  Google Scholar 

  • Zhu, X., & Davidson, I. (2007). Knowledge discovery and data mining: Challenges and realities. New York: IGI Global.

    Book  Google Scholar 

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Authors and Affiliations

  1. College of Communication and Information, University of Tennessee, Knoxville, TN, USA

    Wade Bishop

  2. Center for Spatial Reasoning & Policy Analytics College of Public Service & Community Solutions, Arizona State University, Phoenix, AZ, USA

    Tony H. Grubesic

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  1. Wade Bishop
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  2. Tony H. Grubesic
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Bishop, W., Grubesic, T.H. (2016). Discovery and Fitness for Use. In: Geographic Information. Springer Geography. Springer, Cham. https://doi.org/10.1007/978-3-319-22789-4_7

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