References
- Behrens, T., Viscarra Rossel, R. A., Kerry, R., MacMillan, R., Schmidt, K., Lee, J., Scholten, T., & Zhu, A.-X. (2019). The relevant range of scales for multi-scale contextual spatial modelling. Scientific Report, 9(1), 14800. https://doi.org/https://doi.org/10.1038/s41598-019-51395-3
- Carlson, K., Buttenfield, B., & Qiang, Y. (2020). Visualizing uncertainty metrics across multiple attribute resolutions [Presentation]. AutoCarto 2020, virtual conference.
- Chen, L., Gao, Y., Zhu, D., Yuan, Y., & Liu, Y. (2019). Quantifying the scale effect in geospatial big data using semi-variograms. PLOS ONE, 14(11), e0225139. https://doi.org/https://doi.org/10.1371/journal.pone.0225139
- Chica-Olmo, M., & Abarca-Hernández, F. (2000). Computing geostatistical image texture for remotely sensed data classification. Computers & Geosciences, 26(4), 373–383. https://doi.org/https://doi.org/10.1016/S0098-3004(99)00118-1
- Diggle, P., Rowlingson, B., & Su, T. (2005). Point process methodology for on-line spatio-temporal disease surveillance. Environmetrics, 16(5), 423–434. https://doi.org/https://doi.org/10.1002/env.712
- Duque, J. C., Laniado, H., & Polo, A. (2018). S-maup: Statistical test to measure the sensitivity to the modifiable areal unit problem. PLOS ONE, 13(11), e0207377. https://doi.org/https://doi.org/10.1371/journal.pone.0207377
- Ester, M., Kriegel, H.-P., Sander, J., & Xu, X. (1996). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the second international conference on knowledge discovery and data mining, KDD’96 (pp. 226–231). AAAI Press.
- Fotheringham, A. S., Yang, W., & Kang, W. (2017). Multiscale Geographically Weighted Regression (MGWR). Annals of American Association of Geographers, 107(6), 1247–1265. https://doi.org/https://doi.org/10.1080/24694452.2017.1352480
- Gatrell, A. C., Bailey, T. C., Diggle, P. J., & Rowlingson, B. S. (1996). Spatial point pattern analysis and its application in geographical epidemiology. Transactions of the Institute of British Geographers, 21(1), 256–274. https://doi.org/https://doi.org/10.2307/622936
- Hägerstraand, T. (1970). What about people in regional science? Papers in Regional Science, 24. https://doi.org/https://doi.org/10.1111/j.1435-5597.1970.tb01464.x
- Kraak, M.-J. (2003). The space-time cube revisited from a geovisualization perspective. Proceedings of the 21st international cartographic conference (pp. 1988–1996). International Cartographic Association.
- Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics - Theory and Methods, 26(6), 1481–1496. https://doi.org/https://doi.org/10.1080/03610929708831995
- Kwan, M.-P. (2004). GIS methods in time-geographic research: Geocomputation and geovisualization of human activity patterns. Geografiska Annaler: Series B, Human Geography, 86(4), 267–280. https://doi.org/https://doi.org/10.1111/j.0435-3684.2004.00167.x
- Lam, N. S.-N. (1983). Spatial Interpolation Methods: A Review. The American Cartographer, 10(2), 129–150. https://doi.org/https://doi.org/10.1559/152304083783914958
- Lam, -N. S.-N., Cheng, W., Zou, L., & Cai, H. (2018). Effects of landscape fragmentation on land loss. Remote Sensing of Environment, 209, 253–262. https://doi.org/https://doi.org/10.1016/j.rse.2017.12.034
- Miller, H. J. (1991). Modelling accessibility using space-time prism concepts within geographical information systems. International Journal of Geographical Information Systems, 5(3), 287–301. https://doi.org/https://doi.org/10.1080/02693799108927856
- Mills, P. (2011). Efficient statistical classification of satellite measurements. International Journal of Remote Sensing, 32(21), 6109–6132. https://doi.org/https://doi.org/10.1080/01431161.2010.507795
- Natan, A. (2021, May 26). Fast 2D peak finder. https://github.com/adinatan/fastpeakfind/releases/tag/1.13.0.0
- O’Sullivan, D., & Unwin, D. (2010). Chapter 4: Point pattern analysis. In Geographic information analysis. (pp. 121–155). New Jersey: John Wiley & Sons, Inc., Hoboken.
- Openshaw, S. (1983). The modifiable areal unit problem. In Concepts and Techniques in Modern Geography, 38. Norwich, UK: Geo Books.
- Qiang, Y., Buttenfield, B. P., Lam, N., & De Weghe, N. V. (2018). Novel models for multi-scale spatial and temporal analyses. In S. Winter, A. Griffin, & M. Sester (Eds.), 10th International Conference on Geographic Information Science (GIScience 2018), Leibniz International Proceedings in Informatics (LIPIcs) (pp. 55:1–55:7). Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik. https://doi.org/https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.55.
- Qiang, Y., Chavoshi, S. H., Logghe, S., De Maeyer, P., & Van De Weghe, N. (2014). Multi-scale analysis of linear data in a two-dimensional space. Information Visualization, 13(3), 248–265. https://doi.org/https://doi.org/10.1177/1473871613477853
- Qiang, Y., & Van de Weghe, N. (2019). Re-arranging space, time and scales in GIS: Alternative models for multi-scale spatio-temporal modeling and analyses. ISPRS International Journal of Geo-Information, 8(2), 72. https://doi.org/https://doi.org/10.3390/ijgi8020072
- Rey, S. J., & Anselin, L. (2007). PySAL: A python library of spatial analytical methods. The Review of Regional Studies, 37(1), 5–27. https://doi.org/https://doi.org/10.52324/001c.8285
- Shi, X. (2010). Selection of bandwidth type and adjustment side in kernel density estimation over inhomogeneous backgrounds. International Journal of Geographical Information Science, 24(5), 643–660. https://doi.org/https://doi.org/10.1080/13658810902950625
- Silverman, B. W. (1986). Density estimation for statistics and data analysis. Chapman & Hall.
- Terrell, G. R., & Scott, D. W. (1992). Variable kernel density estimation. The Annals of Statistics, 20(3), 1236–1265. https://doi.org/https://doi.org/10.1214/aos/1176348768
- Tiwari, C., & Rushton, G. (2005). Using spatially adaptive filters to map late stage colorectal cancer incidence in Iowa. In P. F. Fisher (Ed.), Developments in spatial data handling (pp. 665–676). Springer. https://doi.org/https://doi.org/10.1007/3-540-26772-7_50
- Tiwari, C., & Rushton, G. (2010). A spatial analysis system for integrating data, methods and models on environmental risks and health outcomes. Transactions in GIS, 14, 177–195. https://doi.org/https://doi.org/10.1111/j.1467-9671.2010.01220.x
- Van Kerm, P. (2003). Adaptive kernel density estimation. The Stata Journal, 3(2), 148–156. https://doi.org/https://doi.org/10.1177/1536867X0300300204
- Wagner, H. H., & Fortin, M.-J. (2005). Spatial Analysis of Landscapes: Concepts and Statistics. Ecology, 86(8), 1975–1987. https://doi.org/https://doi.org/10.1890/04-0914
- Wu, F. (2002). Calibration of stochastic cellular automata: The application to rural-urban land conversions. International Journal of Geographical Information Science, 16(8), 795–818. https://doi.org/https://doi.org/10.1080/13658810210157769
- Ye, X. (2021). Estimating σ2 for the Classical Linear Regression Model (CLRM) with the presence of the Modifiable Areal Unit Problem (MAUP). Geographical Analysis. https://doi.org/https://doi.org/10.1111/gean.12291
- Ye, X., & Rogerson, P. (2021). The impacts of the Modifiable Areal Unit Problem (MAUP) on omission error. Geographical Analysis. 54(1), 32–57. https://doi.org/https://doi.org/10.1111/gean.12269
- Yin, P. (2020). Kernels and density estimation. In J. P. Wilson (Ed.), The geographic information science & technology body of knowledge (1st Quarter 2020 ed.). https://doi.org/https://doi.org/10.22224/gistbok/2020.1.12
- Yuan, Y., Qiang, Y., Bin Asad, K., & Chow, E. (2020). Point pattern analysis. In J. P. Wilson (Ed.), The geographic information science & technology body of knowledge. https://doi.org/https://doi.org/10.22224/gistbok/2020.1.13