{"id":44293,"date":"2023-08-03T21:32:15","date_gmt":"2023-08-03T13:32:15","guid":{"rendered":"https:\/\/swarma.org\/?p=44293"},"modified":"2023-08-03T21:32:15","modified_gmt":"2023-08-03T13:32:15","slug":"%e5%9c%b0%e7%90%86%e7%a9%ba%e9%97%b4%e4%ba%ba%e5%b7%a5%e6%99%ba%e8%83%bdgeoai%ef%bd%9c%e5%91%a8%e5%85%ad%e7%9b%b4%e6%92%ad%c2%b7%e5%a4%8d%e6%9d%82%e7%b3%bb%e7%bb%9f%e8%a7%86%e8%a7%92%e7%9a%84%e5%9f%8e","status":"publish","type":"post","link":"https:\/\/swarma.org\/?p=44293","title":{"rendered":"\u5730\u7406\u7a7a\u95f4\u4eba\u5de5\u667a\u80fdGeoAI\uff5c\u5468\u516d\u76f4\u64ad\u00b7\u590d\u6742\u7cfb\u7edf\u89c6\u89d2\u7684\u57ce\u5e02\u79d1\u5b66\u8bfb\u4e66\u4f1a"},"content":{"rendered":"
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\u7b2c\u4e00\u90e8\u5206\u4ecb\u7ecd\u5730\u7406\u73b0\u8c61\u7684\u7a7a\u95f4\u6027\u3002\u91cd\u70b9\u8ba8\u8bba\u5730\u7406\u7a7a\u95f4\u7684\u8868\u8fbe\u65b9\u6cd5\uff0c\u7a7a\u95f4\u673a\u7406\u7684\u5efa\u6a21\uff0c\u4ee5\u53ca\u7a7a\u95f4\u63a8\u6d4b\u3002\u5e76\u603b\u7ed3\u9488\u5bf9\u5730\u7406\u73b0\u8c61\u7684\u4e00\u822c\u5206\u6790\u4efb\u52a1\u3002<\/span><\/p>\n

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\u7b2c\u4e94\u90e8\u5206\u5c55\u671b\u57ce\u5e02\u4e2d\u7684GeoAI\u95ee\u9898\u4e0e\u6311\u6218\u3002<\/span><\/section>\n

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        \u56db\u3001\u5730\u7406\u4eba\u5de5\u667a\u80fd\u7684\u57ce\u5e02\u5e94\u7528 \u2013 Where\uff1f<\/strong><\/span><\/section>\n
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          \u4e94\u3001\u57ce\u5e02\u4e2d\u7684GeoAI\u95ee\u9898\u4e0e\u6311\u6218 \u2013 Then\uff1f<\/strong><\/span><\/section>\n
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             \u2022 \u7a7a\u95f4\u8868\u8fbe Spatial representation\uff1a<\/span><\/strong>\u5730\u7406\u79d1\u5b66\u4e2d\u7684\u7a7a\u95f4\u8868\u8fbe\u662f\u6307\u901a\u8fc7\u5404\u79cd\u65b9\u5f0f\u548c\u5de5\u5177\u6765\u63cf\u8ff0\u548c\u5448\u73b0\u5730\u7403\u8868\u9762\u4e0a\u7684\u7a7a\u95f4\u7279\u5f81\u548c\u73b0\u8c61\u3002\u8fd9\u5305\u62ec\u4f7f\u7528\u5730\u56fe\u3001\u56fe\u8868\u3001\u6a21\u578b\u7b49\u624b\u6bb5\u6765\u5c55\u793a\u5730\u7406\u5b9e\u4f53\u7684\u4f4d\u7f6e\u3001\u5206\u5e03\u3001\u5173\u7cfb\u548c\u53d8\u5316\u3002\u901a\u8fc7\u7a7a\u95f4\u8868\u8fbe\uff0c\u5730\u7406\u79d1\u5b66\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u548c\u89e3\u91ca\u5730\u7403\u4e0a\u7684\u81ea\u7136\u548c\u4eba\u6587\u73b0\u8c61\uff0c\u63ed\u793a\u5730\u7406\u6a21\u5f0f\u548c\u89c4\u5f8b\uff0c\u652f\u6301\u5730\u7406\u7814\u7a76\u548c\u51b3\u7b56\u5236\u5b9a\u3002<\/span><\/section>\n
             \u2022 \u7a7a\u95f4\u5173\u7cfb Spatial relationship\uff1a<\/strong>\u7a7a\u95f4\u5173\u7cfb\u53ef\u4ee5\u662f\u7531\u7a7a\u95f4\u5b9e\u4f53\u7684\u51e0\u4f55\u7279\u6027(\u5305\u62ec\u7a7a\u95f4\u7269\u4f53\u7684\u5730\u7406\u4f4d\u7f6e\u4e0e\u5f62\u72b6)\u5f15\u8d77\u7684\u7a7a\u95f4\u5173\u7cfb\uff0c\u5982\u8ddd\u79bb\u3001\u65b9\u4f4d\u3001\u8fde\u901a\u6027\u3001\u76f8\u4f3c\u6027\u7b49\uff1b\u4e5f\u53ef\u4ee5\u662f\u7531\u7a7a\u95f4\u5b9e\u4f53\u7684\u51e0\u4f55\u7279\u6027\u548c\u975e\u51e0\u4f55\u7279\u6027(\u5305\u62ec\u5ea6\u91cf\u5c5e\u6027\u5982\u9ad8\u7a0b\u503c\u3001\u5761\u5ea6\u503c\u3001\u6c14\u6e29\u503c\u7b49\uff0c\u540d\u79f0\u5c5e\u6027\u5982\u5730\u540d\u3001\u7269\u4f53\u540d\u79f0\u7b49)\u5171\u540c\u5f15\u8d77\u7684\u7a7a\u95f4\u5173\u7cfb\uff0c\u5982\u7a7a\u95f4\u5206\u5e03\u73b0\u8c61\u7684\u7edf\u8ba1\u76f8\u5173\u3001\u7a7a\u95f4\u81ea\u76f8\u5173\u3001\u7a7a\u95f4\u76f8\u4e92\u4f5c\u7528\u3001\u7a7a\u95f4\u4f9d\u8d56\u7b49\uff1b\u8fd8\u6709\u4e00\u79cd\u662f\u5b8c\u5168\u7531\u7a7a\u95f4\u5b9e\u4f53\u7684\u975e\u51e0\u4f55\u5c5e\u6027\u6240\u5bfc\u51fa\u7684\u7a7a\u95f4\u5173\u7cfb\uff0c\u5982\u7531\u4e24\u4e2a\u57ce\u5e02\u7684\u4eba\u53e3\u6570\u7684\u6bd4\u8f83\u6240\u4ea7\u751f\u7684\u5927\u5c0f\u5173\u7cfb\u3001\u65f6\u95f4\u4e0a\u7684\u5148\u540e\u5173\u7cfb\u3001\u7b49\u7ea7\u4e0a\u7684\u9ad8\u4f4e\u5173\u7cfb\u4ee5\u53ca\u6210\u56e0\u4e0a\u7684\u56e0\u679c\u5173\u7cfb\u7b49\u90fd\u5c5e\u4e8e\u6b64\u7c7b\u5173\u7cfb \u3002<\/span><\/section>\n
             \u2022 \u7a7a\u95f4\u63a8\u6d4b Spatial prediction\uff1a<\/strong>\u5728\u5730\u7406\u79d1\u5b66\u4e2d\uff0c\u7a7a\u95f4\u63a8\u6d4b\u662f\u4e00\u79cd\u901a\u8fc7\u89c2\u5bdf\u548c\u5206\u6790\u73b0\u6709\u7684\u5730\u7406\u6570\u636e\u548c\u6a21\u5f0f\uff0c\u6765\u63a8\u65ad\u6216\u9884\u6d4b\u672a\u77e5\u5730\u7406\u73b0\u8c61\u7684\u65b9\u6cd5\u3002\u5b83\u57fa\u4e8e\u73b0\u6709\u7684\u6570\u636e\u548c\u6a21\u578b\uff0c\u5229\u7528\u7edf\u8ba1\u5b66\u3001\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\u548c\u7a7a\u95f4\u5206\u6790\u6280\u672f\u7b49\u5de5\u5177\uff0c\u6765\u63a8\u65ad\u672a\u77e5\u5730\u70b9\u3001\u672a\u6765\u72b6\u6001\u6216\u672a\u89c2\u6d4b\u5230\u7684\u5730\u7406\u73b0\u8c61\u3002<\/span><\/section>\n
             \u2022 \u7a7a\u95f4\u663e\u5f0f\u6a21\u578b Spatially explicit model\uff1a<\/strong>\u4e00\u79cd\u7528\u4e8e\u7814\u7a76\u5730\u7406\u7a7a\u95f4\u548c\u5730\u7406\u73b0\u8c61\u7684\u6570\u5b66\u6a21\u578b\u3002\u8be5\u6a21\u578b\u5c06\u5730\u7406\u7a7a\u95f4\u5212\u5206\u4e3a\u79bb\u6563\u7684\u5355\u5143\u6216\u5355\u5143\u683c\uff0c\u5e76\u901a\u8fc7\u5728\u8fd9\u4e9b\u5355\u5143\u4e4b\u95f4\u5b9a\u4e49\u5173\u7cfb\u548c\u4ea4\u4e92\u6765\u63cf\u8ff0\u5730\u7406\u73b0\u8c61\u7684\u5206\u5e03\u548c\u53d8\u5316\u3002\u901a\u8fc7\u4f7f\u7528\u7a7a\u95f4\u663e\u5f0f\u6a21\u578b\uff0c\u6211\u4eec\u53ef\u4ee5\u66f4\u597d\u5730\u7406\u89e3\u548c\u89e3\u91ca\u5730\u7406\u73b0\u8c61\u7684\u7a7a\u95f4\u5206\u5e03\u548c\u76f8\u4e92\u4f5c\u7528\uff0c\u4ece\u800c\u5bf9\u5730\u7406\u7a7a\u95f4\u8fdb\u884c\u66f4\u7cbe\u786e\u7684\u5efa\u6a21\u548c\u5206\u6790\u3002\u5177\u4f53\u6765\u8bf4\uff0c\u5982\u679c\u4e00\u4e2a\u4eba\u5de5\u667a\u80fd\u6a21\u578b\u6ee1\u8db3\u4ee5\u4e0b\u68c0\u9a8c\u4e2d\u7684\u4e00\u6761\uff0c\u5219\u53ef\u4ee5\u79f0\u4e3a\u7a7a\u95f4\u663e\u5f0f\u6a21\u578b\uff1a\uff081\uff09\u4e0d\u53d8\u6027\u68c0\u9a8c\uff1b\uff082\uff09\u8868\u5f81\u68c0\u9a8c\uff1b\uff083\uff09\u516c\u5f0f\u5316\u68c0\u9a8c\uff1b\uff084\uff09\u7ed3\u679c\u68c0\u9a8c\u3002<\/span><\/section>\n
             \u2022 \u5730\u7406\u4eba\u5de5\u667a\u80fd GeoAI\uff1a<\/strong>\u5c06\u4eba\u5de5\u667a\u80fd\u6280\u672f\u5e94\u7528\u4e8e\u5730\u7406\u4fe1\u606f\u7cfb\u7edf\uff08GIS\uff09\u9886\u57df\u7684\u4e00\u79cd\u65b9\u6cd5\u3002\u5229\u7528\u673a\u5668\u5b66\u4e60\u3001\u81ea\u7136\u8bed\u8a00\u5904\u7406\u3001\u56fe\u50cf\u8bc6\u522b\u7b49\u6280\u672f\uff0c\u4ece\u5730\u7406\u6570\u636e\u4e2d\u63d0\u53d6\u6709\u610f\u4e49\u7684\u4fe1\u606f\u5e76\u8fdb\u884c\u5206\u6790\u548c\u51b3\u7b56\uff0c\u4ee5\u652f\u6301\u7a7a\u95f4\u89c4\u5212\u3001\u8d44\u6e90\u7ba1\u7406\u3001\u73af\u5883\u76d1\u6d4b\u3001\u57ce\u5e02\u89c4\u5212\u7b49\u9886\u57df\u7684\u51b3\u7b56\u548c\u9884\u6d4b\u3002\u5730\u7406\u4eba\u5de5\u667a\u80fd\u5c06\u667a\u80fd\u7b97\u6cd5\u4e0e\u5730\u7406\u6570\u636e\u548c\u7a7a\u95f4\u5206\u6790\u76f8\u7ed3\u5408\uff0c\u63d0\u4f9b\u66f4\u9ad8\u6548\u3001\u51c6\u786e\u548c\u667a\u80fd\u7684\u5730\u7406\u4fe1\u606f\u5904\u7406\u548c\u51b3\u7b56\u652f\u6301\u80fd\u529b\u3002<\/span><\/section>\n
             \u2022 \u57ce\u5e02\u793e\u4f1a\u7ecf\u6d4e Socio-economic dimension of cities\uff1a<\/strong>\u57ce\u5e02\u4e2d\u5404\u79cd\u793e\u4f1a\u548c\u7ecf\u6d4e\u6d3b\u52a8\u7684\u7ec4\u5408\u548c\u76f8\u4e92\u5173\u7cfb\u3002\u5b83\u5305\u62ec\u57ce\u5e02\u4eba\u53e3\u7684\u7ec4\u6210\u4e0e\u7ed3\u6784\u3001\u5c31\u4e1a\u4e0e\u6536\u5165\u6c34\u5e73\u3001\u5c45\u6c11\u6d88\u8d39\u548c\u751f\u6d3b\u65b9\u5f0f\u3001\u4ea7\u4e1a\u7ed3\u6784\u548c\u7ecf\u6d4e\u53d1\u5c55\u3001\u793e\u4f1a\u670d\u52a1\u4e0e\u516c\u5171\u8bbe\u65bd\u7b49\u65b9\u9762\u7684\u5185\u5bb9\u3002\u57ce\u5e02\u793e\u4f1a\u7ecf\u6d4e\u7684\u7279\u70b9\u662f\u591a\u6837\u5316\u3001\u590d\u6742\u6027\u548c\u76f8\u4e92\u4f9d\u8d56\u6027\u3002\u57ce\u5e02\u7684\u793e\u4f1a\u7ecf\u6d4e\u6d3b\u52a8\u4e0d\u4ec5\u4ec5\u662f\u7ecf\u6d4e\u751f\u4ea7\u548c\u6d88\u8d39\uff0c\u8fd8\u5305\u62ec\u793e\u4f1a\u5173\u7cfb\u3001\u6587\u5316\u4f20\u7edf\u3001\u6559\u80b2\u3001\u533b\u7597\u7b49\u65b9\u9762\u7684\u5404\u79cd\u6d3b\u52a8\u3002\u901a\u8fc7\u7814\u7a76\u57ce\u5e02\u793e\u4f1a\u7ecf\u6d4e\uff0c\u53ef\u4ee5\u4e86\u89e3\u57ce\u5e02\u53d1\u5c55\u7684\u52a8\u6001\u53d8\u5316\u3001\u57ce\u5e02\u5c45\u6c11\u7684\u751f\u6d3b\u72b6\u51b5\u548c\u9700\u6c42\uff0c\u4e3a\u57ce\u5e02\u89c4\u5212\u548c\u7ba1\u7406\u63d0\u4f9b\u53c2\u8003\u548c\u51b3\u7b56\u652f\u6301\u3002<\/span><\/section>\n


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            \u4e2a\u4eba\u4e3b\u9875 https:\/\/cla.umn.edu\/about\/directory\/profile\/dizhu\u3002<\/span><\/section>\n

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            \u626b\u7801\u53c2\u4e0e\u590d\u6742\u7cfb\u7edf\u89c6\u89d2\u7684\u57ce\u5e02\u79d1\u5b66\u8bfb\u4e66\u4f1a\uff0c\u52a0\u5165\u7fa4\u804a\uff0c\u83b7\u53d6\u7cfb\u5217\u8bfb\u4e66\u4f1a\u56de\u770b\u6743\u9650\uff0c\u6210\u4e3a\u57ce\u5e02\u79d1\u5b66\u793e\u533a\u7684\u79cd\u5b50\u7528\u6237\uff0c\u4e0e\u793e\u533a\u7684\u4e00\u7ebf\u79d1\u7814\u5de5\u4f5c\u8005\u4e0e\u4f01\u4e1a\u5b9e\u8df5\u8005\u6c9f\u901a\u4ea4\u6d41\uff0c\u5171\u540c\u63a8\u52a8\u57ce\u5e02\u79d1\u5b66\u793e\u533a\u7684\u53d1\u5c55\u3002<\/span><\/section>\n

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            \u4e66<\/strong>\uff1a<\/strong><\/span><\/section>\n
             \u2022 Artificial Intelligence in Geography, Openshaw, 1997<\/span><\/section>\n
             \u2022 Handbook of Geospatial Artificial Intelligence, Gao et al, 2023<\/span> forthcoming<\/span><\/span><\/section>\n

            <\/section>\n
            \u4e00\u3001\u5730\u7406\u73b0\u8c61\u7684\u7a7a\u95f4\u6027 \u2013 Why?<\/strong><\/span><\/section>\n
             <\/em><\/span>\u2022 Die Geographie: ihre Geschichte, ihr Wesen und ihre Methoden, A. Hettner, 1927<\/span><\/section>\n
             \u2022 Perspective on the Nature of Geography, R. Hartshorne, 1959<\/span><\/section>\n
             \u2022 Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234\u2013240. https:\/\/doi.org\/10.2307\/143141<\/span><\/section>\n

            <\/strong><\/span><\/section>\n
            \u4e8c\u3001\u7a7a\u95f4\u663e\u5f0f\u5730\u7406\u4eba\u5de5\u667a\u80fd \u2013 What?<\/strong><\/span><\/section>\n
             \u2022 Goodchild, M. (2001). Issues in spatially explicit modeling. Agent-based models of land-use and land-cover change, 13-17.<\/span><\/section>\n
             \u2022 Janowicz, K., Gao, S., McKenzie, G., Hu, Y., & Bhaduri, B. (2020). GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34(4), 625-636. https:\/\/doi.org\/10.1080\/13658816.2019.1684500<\/span><\/section>\n

            <\/section>\n
            \u4e09\u3001\u4eba\u5de5\u667a\u80fd\u4e0e\u5730\u7406\u7a7a\u95f4\u7684\u5185\u5728\u8054\u7cfb \u2013 How?<\/strong><\/span><\/section>\n
             \u2022 Fischer, M. M. (1998). Computational neural networks: a new paradigm for spatial analy<\/span><\/span>sis. Environment and Planning A, 30(10), 1<\/span>873-1891. https:\/\/doi.org\/10.1068\/a301873<\/span><\/span><\/section>\n
             \u2022 \u6731\u9012. \u5730<\/span>\u7406\u73b0\u8c61\u7a7a\u95f4\u5206\u5e03\u7684\u4eba\u5de5\u795e\u7ecf\u7f51\u7edc\u5efa\u6a21\u4e0e\u5206\u6790\u65b9\u6cd5\u7814\u7a76. \u5317\u4eac\u5927\u5b66\u535a\u2f20\u5b66\u4f4d\u8bba\u2f42, 2020.<\/span><\/section>\n
             \u2022 Jean, N., Burke, M., Xie, M., Davis, W. M., Lobell, D. B., & Ermon, S. (2016). Combining satellite imagery and machine learning to predict poverty. Science, 353(6301), 790-794. https:\/\/doi.org\/10.1126\/science.aaf7894<\/span><\/section>\n
             \u2022 Yan, X., Ai, T., Yang, M., & Yin, H. (2019). A graph convolutional neural network for classification of building patterns using spatial vector data. ISPRS journal of photogrammetry and remote sensing, 150, 259-273. https:\/\/doi.org\/10.1016\/j.isprsjprs.2019.02.010<\/span><\/section>\n
             \u2022 Mai, G., Janowicz, K., Hu, Y., Gao, S., Yan, B., Zhu, R., … & Lao, N. (2022). A review of location encoding for GeoAI: methods and applications. International Journal of Geographical Information Science, 36(4), 639-673. https:\/\/doi.org\/10.1080\/13658816.2021.2004602<\/span><\/section>\n
             \u2022 Simini, F., Barlacchi, G., Luca, M., & Pappalardo, L. (2021). A deep gravity model for mobility flows generation. Nature communications, 12(1), 6576. https:\/\/doi.org\/10.1038\/s41467-021-26752-4<\/span><\/section>\n
             \u2022 Zhu, D., Zhang, F., Wang, S., Wang, Y., Cheng, X., Huang, Z., & Liu, Y. (2020). Understanding place characteristics in g<\/span>eographic contexts through graph convolutional neural networks. Annals of the American Association of Geographers, 110(2), 408-420. https:\/\/doi.org\/10.1080\/24694452.2019.1694403<\/span><\/section>\n
            <\/span><\/section>\n
            <\/span><\/section>\n
            \u56db\u3001\u5730\u7406\u4eba\u5de5\u667a\u80fd\u7684\u57ce\u5e02\u5e94\u7528 \u2013 Where\uff1f<\/strong><\/span><\/section>\n
             \u2022 Yuan, Q., Shen, H., Li, T., Li, Z., Li, S., Jiang, Y., … & Zhang, L. (2020). Deep learning in environmental remote sensing: Achievements and challenges. Remote Sensing of Environment, 241, 111716. https:\/\/doi.org\/10.1016\/j.rse.2020.111716<\/span><\/section>\n
             \u2022 Grekousis, G. (2019). Artificial neural networks and deep learning in urban geography: A systematic review and meta-analysis. Computers, Environment and Urban Systems, 74, 244-256. https:\/\/doi.org\/10.1016\/j.compenvurbsys.2018.10.008<\/span><\/section>\n
             \u2022 Casali, Y., Yonca, N. A., Comes, T., & Casali, Y. (2022). Machine learning for spatial analyses in urban areas: a scoping review. Sustainable Cities and Society, 104050. https:\/\/doi.org\/10.1016\/j.scs.2022.104050<\/span><\/section>\n
             \u2022 Liu, P., & Biljecki, F. (2022). A review of spatially-explicit GeoAI applications in Urban Geography. International Journal of Applied Earth Observation and Geoinformation, 112, 102936. https:\/\/doi.org\/10.1016\/j.jag.2022.102936<\/span><\/section>\n
             \u2022 Reichstein, M., Camps-Valls, G., Stevens, B., Jung, M., Denzler, J., & Carvalhais, N. (2019). Deep learning and process understanding for data-driven Earth system science. Nature, 566(7743), 195-204. https:\/\/doi.org\/10.1038\/s41586-019-0912-1<\/span><\/section>\n
             \u2022 Zheng, Y., Capra, L., Wolfson, O., & Yang, H. (2014). Urban computing: concepts, methodologies, and applications. ACM Transactions on Intelligent Systems and Technology (TIST), 5(3), 1-55. https:\/\/doi.org\/10.1145\/2629592<\/span><\/section>\n
             \u2022 Liu, Y., Liu, X., Gao, S., Gong, L., Kang, C., Zhi, Y., … & Shi, L. (2015). Social sensing: A new approach to understanding our socioeconomic environments. Annals of the Association of American Geographers, 105(3), 512-530. https:\/\/doi.org\/10.1080\/00045608.2015.1018773<\/span><\/section>\n

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