Large-scale Geospatial Analytics: Problems, Challenges, and Opportunities

Tsz Nam Chan, Hou U Leong, Byron Choi, Jianliang Xu, Reynold Cheng

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review


Geospatial analytics is an important field in many communities, including crime science, transportation science, epidemiology, ecology, and urban planning. However, with the rapid growth of big geospatial data, most of the commonly used geospatial analytic tools are not efficient (or even feasible) to support large-scale datasets. As such, domain experts have raised the concerns about the inefficiency issues for using these tools. In this tutorial, we aim to arouse the attention of database researchers for this important, emerging, database-related, and interdisciplinary topic, which consists of four parts. In the first part, we will discuss different problems and highlight the challenges for two types of geospatial analytic tools, which are (1) hotspot detection and (2) correlation analysis. In the second and third parts, we will specifically discuss two geospatial analytic tools, namely kernel density visualization (the representative hotspot detection method) and K-function (the representative correlation analysis method), respectively, and their variants. In the fourth part, we will highlight the future opportunities for this topic.
Original languageEnglish
Title of host publicationSIGMOD '23: Companion of the 2023 International Conference on Management of Data
PublisherAssociation for Computing Machinery (ACM)
Number of pages9
ISBN (Electronic)9781450395076
ISBN (Print)9781450395076
Publication statusPublished - 5 Jun 2023
EventACM SIGMOD International Conference on Management of Data, SIGMOD/PODS 2023 - Seattle, United States
Duration: 18 Jun 202323 Jun 2023

Publication series

NameProceedings of the ACM SIGMOD International Conference on Management of Data
ISSN (Print)0730-8078


ConferenceACM SIGMOD International Conference on Management of Data, SIGMOD/PODS 2023
Country/TerritoryUnited States
Internet address

Scopus Subject Areas

  • Software
  • Information Systems

User-Defined Keywords

  • GIS
  • Geospatial analytics
  • K-function
  • efficient algorithm and software development
  • kernel density visualization
  • k-function
  • geospatial analytics


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