1. Singleton, A., Spielman, SE. and Folch D. (2018) Urban Analytics, SAGE Publications Ltd.

Peer Reviewed Journal Articles

  1. Fowler, C, Frey, N, Folch, D.C., Nagle, N, and Spielman SE (2019) Who are the People in Your Neighborhood?  The contextual fallacy.  Geographical Analysis.
  2. Weinberg, D., Abowd, J., Belli, R., Cressie, N., Folch, D., Holan, S., Levenstein, M., Olson, K., Reiter, J., Shapiro, M., Smyth, J., Soh, L-K., Spencer, B., Spielman, SE., Vilhuber, L., Wikle, C.  (2018) Effects of a Government-Academic Partnership: Has the NSF-Census Bureau Research Network Helped Improve the U.S. Statistical System? Journal of Survey Statistics and Methodology
  3. Jurevitch, J., Griffin, AL., Spielman, SE., Folch, DC., Merrick, M. (2018) Navigating Uncertainty:  How Urban and Regional Planners Shape Policy With Fuzzy Data From the American Community Survey.  Journal of the American Planning Association
  4. Folch, DC., Spielman, SE., Manduca, R. (2017) Fast Food Data: Where User Generated Content Works, and Where it Doesn’t.  Geographical Analysis
  5. Spielman, SE., Xiao, N., Cockings, S., and Tanton, R. (2017) Spatial Analysis with Demographic Data: Emerging Issues and Innovative Approaches. Special Issue of Computers, Environment and Urban Systems.
  6. Spielman, SE., Xiao, N., Cockings, S., and Tanton, R. (2017) Statistical Systems and Census Data in the Spatial Sciences. Computers, Environment, and Urban Systems.
  7. Guttman, M., Brown, D., Cunningham, A., Dykes, J., Leonard, S.H., Little J., Mikecz, J, Rhode, P., Spielman, S.E., Sylvester, K.M. (2016)  Migration in the 1930s: Beyond the Dust Bowl.  Social Science History.   **Winner of the Social Science History Founder’s Prize for Best Paper**
  8. Bellman, B., Spielman, S.E., Franklin, R. (2016). Local Population Change and Variations in Racial Integration in the United States, 2000-2010. International Regional Science Review
  9. Folch, D.C., Arribas-Bel, D., Koschinsky, J., and Spielman, S.E. (2016). Uncertain Uncertainty: Spatial Variation in the Quality of American Community Survey Estimates.  Demography
  10. Singleton, A., Brundson, C., and Spielman, S.E. (2015) Establishing a Framework for Open Geographic Information Science.  International Journal of Geographic Information Science.  **IJGIS Most Read Paper 2016 Award**
  11. Spielman, S.E. and Singleton, A. (2015) Studying Neighborhoods with Uncertain Data From the American Community Survey.  Annals of the Association of American Geographers
  12. Wood, N., Jones, J., Spielman, S.E., Schmidtlein, M. (2015) Community clusters of tsunami vulnerability in the U.S. Pacific Northwest. Proceedings of the National Academy of Science
  13. Spielman, S.E. and Folch, D.F. (2015) Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization.  PLOSOne
  14. Spielman, S.E., Folch, D., Nagle, N. (2014) Patterns and Causes of Uncertainty in the American Community Survey.  Applied Geography (replication data and code)
  15. Spielman, S.E. (2014) Spatial Collective Intelligence? credibility, accuracy, and Volunteered Geographic Information.  Cartography and Geographic Information Science (CaGIS)
  16. Folch, D. and Spielman, S.E. (2014) Identifying Regions based on Flexible User Defined Constraints.  International Journal of Geographic Information Science.
  17. Nagle, N., Leyk, S., Buttenfield, B., Spielman, S.E. (2014) Dasymetric Modeling and Uncertainty.  The Annals of the Association of American Geographers.
  18. Singleton, A. and Spielman, S.E. (2014) The Past, Present and Future of Geodemographic Research in the United States and United Kingdom.  The Professional Geographer.
  19. Spielman, S.E. and Harrison, P. (2014).  Residential segregation and the built environment at the turn of the 20th century: a Schelling model.  Transactions in GIS.
  20. Spielman, S.E., Yoo, E.-H. and Linkletter, C. (2013) Neighborhoods and Geographic Patterns in  Health and Behavior: Understanding the Role of Scale and Residential Sorting.  Environment and Planning B. **Winner of the Michael Breheny Prize for Best Paper in EPB**
  21. Spielman, S.E. and Logan, J  (2012). Using high resolution population data to identify neighborhoods and determine their boundaries. Annals of the Association of American Geographers.
  22. Logan, J., Spielman, S.E., Klein, P. (2011) Identifying Ethnic Neighborhoods.  Urban Geography, 32(3)
  23. Spielman, S.E. and Yoo, E-H. (2009). The Spatial Dimensions of Neighborhood Effects. Social Science and Medicine, 68(6)
  24. Spielman, S.E. and Thill, J.C. (2008).  Social Area Analysis, Data Mining, and GIS. Computers, Environment, and Urban Systems, 32(2)
  25. Erdemir, E. T., Batta R., Spielman, S. E., Rogerson P., et. al. (2008).  Optimization of aeromedical base locations in New Mexico. Accident Analysis and Prevention, 40(3)
  26. Erdemir, E.T., Batta, R., Spielman, S.E., Rogerson, P., Blatt, A., and Flanigan, M.A  (2008).  Location Coverage Models with Demand Originating from Nodes and Paths: Application to Cellular Network Design.  European Journal of Operations Research, 190(3)
  27. Borrell, L., Northridge, M. E., Miller, D., Golembeski, C., Spielman, S. E. (2007). Oral Health and Health Care for Older Adults: Addressing Disparities and Planning Services. Special Care in Dentistry, 26(6).
  28. Spielman, S. E. (2006). Appropriate use of the K-function in Urban Environments (Letter). American Journal of Public Health, 96(2).
  29. Spielman, S.E., Golembeski, C.A., Northridge, M.E., et al. (2006). Interdisciplinary Planning for Healthier Communities: Findings from the Harlem Children’s Zone Asthma Initiative.  Journal of the American Planning Association, 72(1).

Working Papers

  1. Fowler, C, Frey, N, Folch, D.C., Nagle, N, and Spielman SE (2018) The Contextual Fallacy. SocArXiv 
  2. Folch, D.C., Arribas-Bel, D., Koschinsky, J., and Spielman, S.E. (2014). Uncertain Uncertainty: Spatial Variation in the Quality of American Community Survey Estimates.  Arizona State University, Geoda Center Working Paper Series.
  3. Spielman, S.E. and Folch, D.C. (2014). Reducing Uncertainty in the American Community Survey through Data-Driven Regionalization.  University of Colorado, Institute of Behavioral Science Working Paper Series (Paper # POP2014-05).

Peer Reviewed Conference Proceedings

  1. Spielman, S.E. (2014)  The Potential for Big Data to Improve Neighborhood-Level Census Data.  Big Data and Urban Informatics.  Chicago, August 2014.

  2. Manduca, R., Spielman, S.E., Folch, D.C. (2014)  Phoenix is Cool: Administrative and Big Data complement each other.  Big Data and Urban Informatics.  Chicago, August 2014.

  3. Spielman, S.E. (2012) Exceptions to the Law: Negative Spatial Autocorrelation in Egocentric Spatial Analysis. GIScience 2012. (extended abstract)
  4. Spielman, S.E. (2012) Does Lifestyle Structure Activity Space? GIScience 2012. (extended abstract)
  5. Spielman, S.E.,  David Folch, John Logan, Nicholas N. Nagle (2012) Thinking inside the box: mapping the microstructure of urban environments (and why it matters).  AutoCarto 2012.
  6. Nagle, N., Buttenfield, B., Leyk, S., and Spielman, S.E. (2012) An Uncertainty-Informed Penalized Maximum Entropy Dasymetric Model GIScience 2012. (extended abstract)
  7. Spielman, S.E.  (2011) Time-Geography and the Twilight Zone, Proceedings of ISPRS-ICA Joint Commission on Geospatial Analysis and Modeling.
  8. Spielman, S.E., Yoo, E.-H. and Linkletter, C. (2011) Understanding the Role of Scale and Sorting in the  Estimation of Neighborhood Effects, Proceedings of the 17th European Colloquium on Quantitative and Theoretical Geography.
  9. Spielman, S.E. and Logan, J. (2011) Western Regional Science Association Annual Meeting, Identifying neighborhoods.

Book Chapters

  1. Spielman, S.E. (In press)  Point Pattern Analysis.  International Encyclopedia of Geography.

  2. Spielman, S.E. (2016) Using big data to improve neighborhood level estimates from the American Community Survey. Chapter X in Seeing Cities Through Big Data – Research, Methods and Applications in Urban Informatics.
  3. Spielman, S.E., Folch, D.C. (2015)  Social Area Analysis with Self-Organizing Maps.  Chapter 9 in Geocomputation Singleton and Brundson (eds.).

Other Publications

  1. Department of Interior Strategic Sciences Group, Operation Group Sandy, Technical Report (2013), Washington, D.C. 75p. 

Currently in Review

  1. Does the Social Vulnerability Index (SoVI) Actually Measure Social Vulnerability?
    1. A critical analysis of the Social Vulnerability Index.  Our paper demonstrates fragility and logical inconsistencies in a widely used social indicator.
  2. Meso-Scale Mobility: observing residential mobility in China using a novel form of big data.
    1. Using novel forms of big data to fill gaps in a national census.  We identify methods for measuring residential mobility at high temporal and spatial resolutions from big data.
  3. Generalized Activity Spaces: a demographic structure in urban activity patterns.
    1. Do similar types of people use urban space in similar ways?  Can we develop archetypical patterns in human mobility conditional on demographic, economic, and contextual factors?  In this paper we develop a novel kind of segmentation for human activity patterns and show that individual characteristics may trump locational characteristics in determining travel patterns.