The College of Arts and Sciences

GeoDa and Spatial Statistics Workshop

The Institute for European and Mediterranean Archaeology (IEMA) and the Geography Department held a co-sponsored workshop event on February 26th, from 1 pm to 3:30 pm.

 

Dr. Peter Rogerson (Geography Department – University at Buffalo) presented an overview of the use of spatial statistics along with current trends in research. This lecture was followed by a hands-on workshop using GeoDa, a powerful analytical tool to visualize and analyze spatial data.
  • Workshop ResourcesAccess to the recorded workshop: https://buffalo.box.com/s/lu4zi17w1oowlzvmyzd3n8o3z52mn59l

    GeoDa Cheatsheet: https://geodacenter.github.io/cheatsheet.html

    GeoDa YouTube Channel: https://www.youtube.com/channel/UCzvhOfSmJpRsFRF2Pgrv-Wg

    GeoDa Workbook: https://www.geos.ed.ac.uk/~gisteac/fspat/geodaworkbook.pdf

     

    Further Reading with Spatial Statistics:

    1. Anselin, L. (1995). Local indicators of spatial association—LISA. Geographical analysis, 27(2), 93-115.
    2. Cliff, A. D., & Ord, J. K. (1981). Spatial processes: models & applications. Taylor &
    3. Carrijo, T. B., & da Silva, A. R. (2017). Modified Moran’s I for Small  Geographical Analysis, 49(4), 451-467.
    4. Clark, P. J., & Evans, F. C. (1954). Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology, 35(4), 445-453.
    5. Kvamme, K. L. (1990). Spatial autocorrelation and the Classic Maya collapse revisited: Refined techniques and new conclusions. Journal of Archaeological       Science, 17(2), 197-207.
    6. Moran, P. A. (1948). The interpretation of statistical maps. Journal of the Royal Statistical Society. Series B (Methodological), 10(2), 243-251.
    7. Premo, L. S. (2004). Local spatial autocorrelation statistics quantify multi-scale patterns in distributional data: an example from the Maya Lowlands. Journal of Archaeological Science, 31(7), 855-866.
    8. Rogerson, P., & Yamada, I. (2008). Statistical detection and surveillance of geographic clusters. Chapman and Hall/CRC.
    9. Rogerson, P. (2010). “Optimal Geographic Scales for Local Spatial ”  Statistical Methods in Medical Research 20(2), 119-29.