Highlights of Research Work

PhD Research: Adaptive Discovery of Geographical Co-occurrence Patterns, Sep. 2014 – Dec. 2019

Investigate the statistical models to adaptively discover spatio-temporal co-occurrence patterns among geographical phenomena (e.g. crimes, species, and diseases) by fully considering the autocorrelation, heterogeneity and multi-scale characteristics of geographical data.

• Nonparametric Test for Detecting Significant Spatial Co-location Patterns

Abstract: Due to the induced spatial auto-correlation among different features, they tend to be located together in close geographic proximity. Spatial co-location patterns are useful for understanding positive spatial interactions among different geographical phenomena. (Read more)

Recommended citation:

1. Jiannan Cai, Min Deng, Qiliang Liu, Zhanjun He, Jianbo Tang and Xuexi Yang., 2019. Nonparametric significance test for discovery of network-constrained spatial co-location patterns. Geographical Analysis, 51(1), 3-22. (Link)

2. Min Deng, Zhanjun He, Qiliang Liu, Jiannan Cai and Jianbo Tang., 2017. Multi‐scale approach to mining significant spatial co‐location patterns. Transactions in GIS, 21(5), 1023-1039. (Link)

• Adaptive Discovery of Multi-level Spatial Co-location Patterns

Abstract: Because of spatial heterogeneity, spatial co-location patterns are usually geographically regional. Regional spatial co-location patterns can be represented as a collection of spatial features that are frequently located together in certain localities (i.e. sub-regions) in the study area. The discovery of regional co-location patterns can facilitate the understanding of the spatial dependency of different spatial features at the micro-scales. (Read more)

Recommended citation:

1. Jiannan Cai, Qiliang Liu, Min Deng, Jianbo Tang and Zhanjun He., 2018. Adaptive detection of statistically significant regional spatial co-location patterns. Computers, Environment and Urban Systems, 68, 53-63. (Link)

2. Min Deng, Jiannan Cai, Qiliang Liu, Zhanjun He and Jianbo Tang., 2017. Multi-level method for discovery of regional co-location patterns. International Journal of Geographical Information Science, 31(9), 1846-1870. (Link)