An Open Source Toolkit for Identifying Comparative Space-time Research Questions

作     者：YE Xinyue SHE Bing WU Ling ZHU Xinyan CHENG Yeqing 

作者机构：Computational Social Science Lab & Department of GeographyKent State University State Key Lab of Information Engineering in Surveying Mapping and Remote SensingWuhan University College of Criminal JusticeZhongnan University of Economics and Law Northeast Institute of Geography and AgroecologyChinese Academy of Sciences 

出 版 物：《Chinese Geographical Science》 (中国地理科学（英文版）)

年 卷 期：2014年第24卷第3期

页      面：348-361页

核心收录：

学科分类：0303[法学-社会学] 12[管理学] 1204[管理学-公共管理] 03[法学] 030301[法学-社会学] 

基　　金：Under the auspices of Humanities and Social Science Research,Major Project of Chinese Ministry of Education(No.13JJD790008) Basic Research Funds of National Higher Education Institutions of China(No.2722013JC030) Zhongnan University of Economics and Law 2012 Talent Grant(No.31541210702) Key Research Program of Chinese Academy of Sciences(No.KZZD-EW-06-03,KSZD-EW-Z-021-03) National Key Science and Technology Support Program of China(No.2012BAH35B03) 

主　　题：open source comparative spatiotemporally integrated social sciences 

摘      要：Comparative space-time thinking lies at the heart of spatiotemporally integrated social sciences. The multiple dimensions and scales of socioeconomic dynamics pose numerous challenges for the application and evaluation of public policies in the comparative context. At the same time, social scientists have been slow to adopt and implement new spatiotemporally explicit methods of data analysis due to the lack of extensible software packages, which becomes a major impediment to the promotion of spatiotemporal thinking. The proposed framework will address this need by developing a set of research questions based on space-time-distributional features of socioeconomic datasets. The authors aim to develop, evaluate, and implement this framework in an open source toolkit to comprehensively quantify the changes and level of hidden variation of space-time datasets across scales and dimensions. Free access to the source code allows a broader community to incorporate additional advances in perspectives and methods, thus facilitating interdisciplinary collaboration. Being written in Python, it is entirely cross-platform, lowering transmission costs in research and education.