skip to main content
10.1145/3356470.3365528acmconferencesArticle/Chapter ViewAbstractPublication PagesgisConference Proceedingsconference-collections
research-article

Spatiotemporal simulation: local Ripley's K function parameterizes adaptive kernel density estimation

Published: 05 November 2019 Publication History

Abstract

The confluence of spatial statistics and simulation has stimulated the study of geographic phenomena for decades. However, the inclusion of the temporal dimension has been insufficiently addressed within the domain of geographic information science. This research focuses on spatiotemporal point pattern analysis to gain insight into this issue and discusses new approaches to facilitate their visualization and analysis in space and time. These methods provide support for statistically robust estimation on spatiotemporally explicit characteristics of point patterns using Monte Carlo simulation. We present a case study featuring spatiotemporal point pattern analysis within the context of dengue fever in the city of Cali, Colombia. Specifically, we use local Ripley's K function to estimate the spatiotemporal signature of dengue and find the scales at which clustering is most significant. We use this information to select bandwidths for adaptive space-time kernel density estimation. The analysis results indicate that simulation is pivotal to promoting our understanding of space-time complexity in dynamic spatial phenomena, represented by the dengue fever.

References

[1]
Sagl, G., Delmelle, E., & Delmelle, E. (2014). Mapping collective human activity in an urban environment based on mobile phone data. Cartography and Geographic Information Science, 41(3), 272--285.
[2]
Owusu, C, Lan, Y, Zheng, M., Tang, W, & Delmelle, E. (2017). Geocoding fundamentals and associated challenges. Geospatial Data Science Techniques and Applications, 41--62.
[3]
Kwan, M. P., & Neutens, T. (2014). Space-time research in GIScience. International Journal of Geographical Information Science, 28(5), 851--854.
[4]
Benenson, I., & Torrens, P. (2004). Geosimulation: Automata-based modeling of urban phenomena. John Wiley & Sons.
[5]
Tang, W., & Bennett, D. A. (2010). Agent-based modeling of animal movement: a review. Geography Compass, 4(7), 682--700.
[6]
Goodchild, M. F. (2013). Prospects for a space-time GIS: Space-time integration in geography and GIScience. Annals of the Association of American Geographers, 103(5), 1072--1077.
[7]
Cressie, N., & Wikle, C. K. (2015). Statistics for spatio-temporal data. John Wiley & Sons.
[8]
Kelly, M., & Meentemeyer, R. K. (2002). Landscape dynamics of the spread of sudden oak death. Photogrammetric Engineering and Remote Sensing, 68(10), 1001--1010.
[9]
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.
[10]
Delmelle, E., C. Dony, I. Casas, M. Jia, and W. Tang. 2014. Visualizing the impact of space-time uncertainties on dengue fever patterns. International Journal of Geographical Information Science 28 (5): 1107--1127.
[11]
Nakaya, T., & Yano, K. (2010). Visualising Crime Clusters in a Space-time Cube: An Exploratory Data-analysis Approach Using Space-time Kernel Density Estimation and Scan Statistics. Transactions in GIS, 14(3), 223--239.
[12]
Brown, D. G., Riolo, R., Robinson, D. T., North, M., & Rand, W. (2005). Spatial process and data models: Toward integration of agent-based models and GIS. Journal of Geographical Systems, 7(1), 25--47.
[13]
Smith, D. M. 2012. Simulating spatial health inequalities. In Agent-based models of geographical systems, 499--510: Springer.
[14]
Yang, Y., and P. Atkinson. 2005. An integrated ABM and GIS model of infectious disease transmission. Computers in Urban Planning and Urban Management-CUPUM'05; 29 June-1 July; London, England.
[15]
Alam, S. J., and R. Meyer. 2010. Comparing two sexual mixing schemes for modelling the spread of HIV/AIDS. In Simulating Interacting Agents and Social Phenomena, 65--76: Springer.
[16]
Stevens, D., & Dragićević, S. (2007). A GIS-based irregular cellular automata model of land-use change. Environment and Planning B: Planning and Design, 34(4), 708--724.
[17]
Batty, M. (1976). Urban modelling (pp. 20--48). Cambridge: Cambridge University Press.
[18]
Guan, D., Li, H., Inohae, T., Su, W., Nagaie, T., & Hokao, K. (2011). Modeling urban land use change by the integration of cellular automaton and Markov model. Ecological Modelling, 222(20--22), 3761--3772.
[19]
Kulldorff, M., & Hjalmars, U. (1999). The Knox method and other tests for space-time interaction. Biometrics, 55(2), 544--552.
[20]
Mantel, N. (1967). The detection of disease clustering and a generalized regression approach. Cancer research, 27(2 Part 1), 209--220.
[21]
Kulldorff, M. (1997). A spatial scan statistic. Communications in Statistics-Theory and methods, 26(6), 1481--1496.
[22]
Best, N., Richardson, S., & Thomson, A. (2005). A comparison of Bayesian spatial models for disease mapping. Statistical methods in medical research, 14(1), 35--59.
[23]
Hohl, A, Zheng, M., Tang, W, Delmelle, E, & Casas, I. (2017). Spatiotemporal Point Pattern Analysis Using Ripley's K Function. In: Karimi, H. A. & Karimi, B. (Eds.) Geospatial Data Science: Techniques and Applications. Taylor & Francis.
[24]
Cali S. Historia del dengue en Cali. Endemia o una continua epidemia. Cali: Secretaria de Salud Publica Municipal de Cali; 2010.
[25]
WHO, 2019. Dengue and Severe Dengue. Geneva, Switzerland: World Health Organization. Available at: http://www.who.int/mediacentre/factsheets/fs117/en/. Accessed October 2, 2019.

Cited By

View all
  • (2023)Estimating spatiotemporal aggregation scales by revisiting the spatiotemporal L‐functionTransactions in GIS10.1111/tgis.13034Online publication date: 5-Mar-2023
  • (2022)HFUL: a hybrid framework for user account linkage across location-aware social networksThe VLDB Journal10.1007/s00778-022-00730-832:1(1-22)Online publication date: 5-Feb-2022
  • (2020)GeoSim 2019 workshop report: The 2nd ACM SIGSPATIAL International Workshop on Geospatial SimulationSIGSPATIAL Special10.1145/3383653.338366111:3(20-22)Online publication date: 13-Feb-2020

Recommendations

Comments

Information & Contributors

Information

Published In

cover image ACM Conferences
GeoSim '19: Proceedings of the 2nd ACM SIGSPATIAL International Workshop on GeoSpatial Simulation
November 2019
46 pages
ISBN:9781450369565
DOI:10.1145/3356470
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Sponsors

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 05 November 2019

Permissions

Request permissions for this article.

Check for updates

Author Tags

  1. Monte Carlo
  2. Ripley's K
  3. kernel density estimation
  4. simulation
  5. space-time

Qualifiers

  • Research-article

Conference

SIGSPATIAL '19
Sponsor:

Acceptance Rates

GeoSim '19 Paper Acceptance Rate 7 of 10 submissions, 70%;
Overall Acceptance Rate 16 of 24 submissions, 67%

Contributors

Other Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

  • Downloads (Last 12 months)24
  • Downloads (Last 6 weeks)2
Reflects downloads up to 17 Jan 2025

Other Metrics

Citations

Cited By

View all
  • (2023)Estimating spatiotemporal aggregation scales by revisiting the spatiotemporal L‐functionTransactions in GIS10.1111/tgis.13034Online publication date: 5-Mar-2023
  • (2022)HFUL: a hybrid framework for user account linkage across location-aware social networksThe VLDB Journal10.1007/s00778-022-00730-832:1(1-22)Online publication date: 5-Feb-2022
  • (2020)GeoSim 2019 workshop report: The 2nd ACM SIGSPATIAL International Workshop on Geospatial SimulationSIGSPATIAL Special10.1145/3383653.338366111:3(20-22)Online publication date: 13-Feb-2020

View Options

Login options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Media

Figures

Other

Tables

Share

Share

Share this Publication link

Share on social media