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Exploring movement object patterns

Author

Listed:
  • Alan Murray
  • Yin Liu
  • Sergio Rey
  • Luc Anselin

Abstract

Change and movement across space and over time are observed in our everyday lives, with people commuting, traveling, communicating, moving, migrating, etc. Understanding how and why such change occurs is important for various reasons, including management of resources, planning for service improvements, detecting whether there are anomalies of some sort, etc. The analysis of spatial information associated with change and movement continues to be supported by a range of techniques, most notably cartography-based exploratory methods. Somewhat lacking, however, are confirmatory and predictive methods to support such analysis. This paper details a suite of approaches implemented in the Python programming language for exploratory analysis, as well as measures that enable statistical testing for pattern significance. Application results for housing movement in an urban region are used to demonstrate the efficacy and functionality of these methods. Copyright Springer-Verlag 2012

Suggested Citation

  • Alan Murray & Yin Liu & Sergio Rey & Luc Anselin, 2012. "Exploring movement object patterns," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 49(2), pages 471-484, October.
    • Handle: RePEc:spr:anresc:v:49:y:2012:i:2:p:471-484
    DOI: 10.1007/s00168-011-0459-z
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    References listed on IDEAS

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    1. Luc Anselin & Shuming Bao, 1997. "Exploratory Spatial Data Analysis Linking SpaceStat and ArcView," Advances in Spatial Science, in: Manfred M. Fischer & Arthur Getis (ed.), Recent Developments in Spatial Analysis, chapter 3, pages 35-59, Springer.
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    Cited by:

    1. Zhaolan Wang & Li Wang & Biao Zhao & Qian Pei, 2023. "Analysis of Spatiotemporal Interaction Characteristics and Decoupling Effects of Urban Expansion in the Central Plains Urban Agglomeration," Land, MDPI, vol. 12(4), pages 1-21, March.
    2. Sébastien Breau & Michael Shin & Nick Burkhart, 2018. "Pulling apart: new perspectives on the spatial dimensions of neighbourhood income disparities in Canadian cities," Journal of Geographical Systems, Springer, vol. 20(1), pages 1-25, January.
    3. Yilin Chen & Dohèto Othniel Kpoviessi & Harry Aginta, 2023. "Investigating regional income convergence in China: an exploratory spatio-temporal perspective," Letters in Spatial and Resource Sciences, Springer, vol. 16(1), pages 1-18, December.
    4. Scott N Lieske & Simone Z Leao & Lindsey Conrow & Chris Pettit, 2021. "Assessing geographical representativeness of crowdsourced urban mobility data: An empirical investigation of Australian bicycling," Environment and Planning B, , vol. 48(4), pages 775-792, May.
    5. Miaoyi Li & Xinyue Ye & Shanqi Zhang & Xiaoyong Tang & Zhenjiang Shen, 2018. "A framework of comparative urban trajectory analysis," Environment and Planning B, , vol. 45(3), pages 489-507, May.
    6. Huijuan Fu & Bo Li & Xiuqing Liu & Jiayi Zheng & Shanggang Yin & Haining Jiang, 2023. "Spatio-Temporal Coupling Evolution of Urbanisation and Carbon Emission in the Yangtze River Economic Belt," IJERPH, MDPI, vol. 20(5), pages 1-19, March.
    7. Siyou Xia & Yu Yang & Xiaoying Qian & Xin Xu, 2022. "Spatiotemporal Interaction and Socioeconomic Determinants of Rural Energy Poverty in China," IJERPH, MDPI, vol. 19(17), pages 1-15, August.

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    More about this item

    Keywords

    C10; R20;

    JEL classification:

    • C10 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - General
    • R20 - Urban, Rural, Regional, Real Estate, and Transportation Economics - - Household Analysis - - - General

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