全球七大洲国家城市三级联动json

全球城市数字化转型的典型案例分析及对上海的启示.pdf

全球主要城市数据列表，该xls包含了世界各国国家省市区(州市区)名字数据列，包含国家名、代码、中英文名称、层级关系等字段

2019年全球主要城市1.5万个地理坐标及字段说明，包括全球223个国家的首都和主要城市的经纬度坐标信息。

该资源为2020.07.28 最新版全球城市IP离线库，可直接使用，使用方式参考：https://blog.csdn.net/xu_cxiang/article/details/106336106

该资源为2020.06最新版全球城市ip离线库，可直接使用，使用方式参考：https://blog.csdn.net/xu_cxiang/article/details/106336106

The mapped global annual urban dynamics (GAUD) and green recovery from 1985 to 2015 at a 30-m resolution. This part of data is organized by 10-degree grids (totally 224). Shapefiles of 10-degree grids can be found in "grids_world.zip". Urban expansion data is packaged in "urban_grid_i.zip" (i ranges from 0 to 223). Green recovery data is packaged in "green_grid_0-223.zip". Their format is GeoTiff, and for each pixel, values from 1985 to 2015 demonstrate the urbanized or green recovery year, while 0 means no data. (2) The interpreted samples of urban extent in 1985 and 2015, and urbanized year during 1985 and 2015. This part of data is for examining the accuracies of our data fusion and temporal segmentation approach. Interpreted urban extent is packaged in "Ref_tif_clip_1985.rar" and "Ref_tif_clip_2015.rar". Its format is GeoTiff, and for each pixel, value 1 means urban areas, while 0 means non-urban areas. Valid samples of urbanized year can be found in "validation_urbanized_year.xls". (3) A demo of NUACI calculation and urbanized years dectection can be found at link: https://code.earthengine.google.com/1c901129fa8c9d81b292824e8fb4ff1c

全球城市地区表 CREATE TABLE `tbl_country` ( `id` int(11) NOT NULL AUTO_INCREMENT, `country` varchar(50) COLLATE utf8_bin DEFAULT NULL, `state` varchar(50) COLLATE utf8_bin DEFAULT NULL, `city` varchar(50) COLLATE utf8_bin DEFAULT NULL, `region` varchar(50) COLLATE utf8_bin DEFAULT NULL, `code` varchar(50) COLLATE utf8_bin DEFAULT NULL, `parentid` int(11) DEFAULT NULL, PRIMARY KEY (`id`) )

找了很多资料，自己整理了一份，我的数据库时mysql的，取用时各自转换一下。

全球各城市经纬度，可用于天气等开发！