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A framework for exploratory space-time analysis of economic data

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  • Xinyue Ye
  • Sergio Rey

Abstract

The development of exploratory spatial data analysis methods is an active research domain in the field of geographic information science (GIS). At the same time, the coupled space-time attributes of economic phenomena are difficult to be represented and examined. Both GIS and economic geography are faced with the challenges of dealing with the temporal dynamics of geographic processes and spatial dynamics of economic development across scales and dimensions. This paper thus suggests a novel way to generalize the characteristics and the structure of space-time data sets, using regional economic data as the example. Accordingly, a reasonable number of general questions (data analysis tasks) can be abstracted. Then, tools (methods) may be suggested on that basis. The cross-fertilization between exploratory spatial data analysis (ESDA) and spatial economics is also identified and illustrated by the capabilities of these components, which have uncovered some interesting patterns and trends in the spatial income data of China and the United States. Through exploratory analysis of economic data, the detection of rich details of underlying geographical and temporal processes would be the first step toward such cross-fertilization. In addition, this exploratory analytical framework can be applied to other data sets that are also measured for areal units at multiple points in time. Copyright Springer-Verlag 2013

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  • Xinyue Ye & Sergio Rey, 2013. "A framework for exploratory space-time analysis of economic data," The Annals of Regional Science, Springer;Western Regional Science Association, vol. 50(1), pages 315-339, February.
    • Handle: RePEc:spr:anresc:v:50:y:2013:i:1:p:315-339
    DOI: 10.1007/s00168-011-0470-4
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    Cited by:

    1. Zheye Wang & Yeqing Cheng & Xinyue Ye & Y. H. Dennis Wei, 2016. "Analyzing the Space–Time Dynamics of Innovation in China: ESDA and Spatial Panel Approaches," Growth and Change, Wiley Blackwell, vol. 47(1), pages 111-129, March.
    2. Yuan, Feng & Wu, Jiawei & Wei, Yehua Dennis & Wang, Lei, 2018. "Policy change, amenity, and spatiotemporal dynamics of housing prices in Nanjing, China," Land Use Policy, Elsevier, vol. 75(C), pages 225-236.
    3. Ángeles Sánchez & Jorge Chica-Olmo & Juan de Dios Jiménez-Aguilera, 2018. "A Space–Time Study for Mapping Quality of Life in Andalusia During the Crisis," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 135(2), pages 699-728, January.
    4. Zhiyuan Zhu & Zhenzhong Dai & Shilin Li & Yongzhong Feng, 2022. "Spatiotemporal Evolution of Non-Grain Production of Cultivated Land and Its Underlying Factors in China," IJERPH, MDPI, vol. 19(13), pages 1-15, July.
    5. Juan C. Duque & Xinyue Ye & David C. Folch, 2015. "spMorph: An exploratory space-time analysis tool for describing processes of spatial redistribution," Papers in Regional Science, Wiley Blackwell, vol. 94(3), pages 629-651, August.
    6. Jie Gu & Suhong Zhou & Xinyue Ye, 2016. "Uneven Regional Development Under Balanced Development Strategies: Space-Time Paths of Regional Development in Guangdong, China," Tijdschrift voor Economische en Sociale Geografie, Royal Dutch Geographical Society KNAG, vol. 107(5), pages 596-610, December.
    7. Wei Shen & Yang Li, 2022. "Multi-Scale Assessment and Spatio-Temporal Interaction Characteristics of Ecosystem Health in the Middle Reaches of the Yellow River of China," IJERPH, MDPI, vol. 19(23), pages 1-21, December.
    8. Yiheng Zhu & Shan Yang & Jinping Lin & Shanggang Yin, 2022. "Spatial and Temporal Evolutionary Characteristics and Its Influencing Factors of Economic Spatial Polarization in the Yangtze River Delta Region," IJERPH, MDPI, vol. 19(12), pages 1-20, June.
    9. Jin Hu & Xuelei Xiong & Yuanyuan Cai & Feng Yuan, 2020. "The Ripple Effect and Spatiotemporal Dynamics of Intra-Urban Housing Prices at the Submarket Level in Shanghai, China," Sustainability, MDPI, vol. 12(12), pages 1-17, June.
    10. Yaobin Wang & Ruitao Zhao & Ying Li & Rong Yao & Ruoxue Wu & Wenlin Li, 2023. "Spatial and Temporal Heterogeneity of Rural Habitat Level Evolution and Its Influencing Factors—A Case Study of Rural Villages in Nature a Reserve of China," Sustainability, MDPI, vol. 15(7), pages 1-25, March.
    11. Jun Fu & Rui Ding & Yilin Zhang & Tao Zhou & Yiming Du & Yuqi Zhu & Linyu Du & Lina Peng & Jian Zou & Wenqian Xiao, 2022. "The Spatial-Temporal Transition and Influencing Factors of Green and Low-Carbon Utilization Efficiency of Urban Land in China under the Goal of Carbon Neutralization," IJERPH, MDPI, vol. 19(23), pages 1-25, December.
    12. Huangling Gu & Yan Liu & Hao Xia & Xiao Tan & Yanjia Zeng & Xianchao Zhao, 2023. "Spatiotemporal Dynamic Evolution and Its Driving Mechanism of Carbon Emissions in Hunan Province in the Last 20 Years," IJERPH, MDPI, vol. 20(4), pages 1-25, February.
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    14. 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.
    15. Chenglin Qin & Xinyue Ye & Yingxia Liu, 2017. "Spatial Club Convergence of Regional Economic Growth in Inland China," Sustainability, MDPI, vol. 9(7), pages 1-14, July.
    16. Shengqiang Jing & Yueguan Yan & Fangqu Niu & Wenhui Song, 2022. "Urban Expansion in China: Spatiotemporal Dynamics and Determinants," Land, MDPI, vol. 11(3), pages 1-16, February.

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    Keywords

    C21; C88;

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • C88 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs - - - Other Computer Software

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