4-空间数据分析/4.2-【ArcGIS Python系列】/4.2.12-实操1-如何将ArcGIS Pro中的统一坐标系工作转换为Python自动化流程.ipynb

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   "source": [
    "# 实操1 如何将ArcGIS Pro中的统一坐标系工作转换为Python自动化流程\n",
    "本notebook整理自[使用 Python 自动化地理处理工作流](https://learn.arcgis.com/zh-cn/projects/automate-a-geoprocessing-workflow-with-python/)，原文为网页，本文档为notebook形式，加上了代码注释，方便学习和实操。"
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   "source": [
    "试想一下，你是一名GIS工作新人，你的领导总是让你做一些基础的工作，这一次他交给你政府和甲方提供的shp格式用地数据、兴趣点数据、街道数据等，你需要将一些数据集转换为统一的坐标系，然后将其导入到地理数据库中。你可能会在ArcGIS Pro中手动完成这些工作，但是如果你需要重复这些工作，那么手动完成这些工作就会变得很繁琐。在这种情况下，你可以使用30行Python代码自动化完成这些工作流程，然后你就可以摸鱼了。。。"
   ],
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   "cell_type": "markdown",
   "source": [
    "本次演示文件在data5文件夹中，包含工程文件和一个地理数据库，数据库中的要素类和交通运输有关，假设您需要根据这些要素类构建网络。 为了实现该目的，这些要素类需要存储在相同的要素数据集中，并且具有相同的坐标系。当前情况并非如此。\n",
    "![数据库结构](https://image-1315363329.cos.ap-shanghai.myqcloud.com/lessons/202308241545547.png)\n"
   ],
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   "cell_type": "markdown",
   "source": [
    "在本教程中，您将使用 Python 代码检查要素类的坐标系，并使用投影工具对所有当前不在正确坐标系中的数据集进行转换，从而将其复制到新地理数据库和要素数据集。 您可以通过此操作创建一个网络，但您在本课程中不会构建网络。"
   ],
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   "cell_type": "markdown",
   "source": [
    "在处理 Python 代码之前，你可以打开名Workflow.aprx的工程文件，可以试着手动检查一下数据库中某个要素类的坐标系并手动运行投影工具。 这一步是必须的，因为借此能了解使用Arcpy时的工作流程。\n",
    "手动流程提示：检查坐标系，并决定是否需要运行投影工具。 投影必须进行投影的要素类。 在不进行投影的情况下复制其他要素类。\n",
    "1.检查图层和地图的坐标系：右键选择属性-选择源-空间参考-查看坐标系：有的为NAD 1983，有的为WGS 1984\n",
    "2.选择投影工具-选择输入要素类-选择投影坐标系-确定-等待完成"
   ],
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   "cell_type": "markdown",
   "source": [
    "### 图层属性操作界面\n",
    "![图层属性](https://learn.arcgis.com/zh-cn/projects/automate-a-geoprocessing-workflow-with-python/GUID-6A8EE57E-3B9C-4A43-9E8E-BC2CAD0235BC-web.png)\n",
    "例如，bike_racks 要素类位于名为 WGS 1984 的地理坐标系中，而 roads 和 boundary 要素类位于经过投影的美国国家平面坐标系（马里兰州）NAD 1983 StatePlane Maryland FIPS 1900 (US Feet) 中。"
   ],
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   "source": [
    "### 投影工具操作界面\n",
    "![投影工具](https://learn.arcgis.com/zh-cn/projects/automate-a-geoprocessing-workflow-with-python/GUID-47BEA67E-8BC6-4106-8EF6-E9A03C685586-web.png)\n"
   ],
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   "source": [
    "为了不重复这些步骤，我们用 Python 代码自动化完成此过程。"
   ],
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   "source": [
    "## 检查坐标系\n",
    "目的是将数据库中所有要素类的坐标都投影到指定坐标系："
   ],
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  {
   "cell_type": "code",
   "execution_count": 26,
   "outputs": [
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     "name": "stdout",
     "output_type": "stream",
     "text": [
      "C:\\Users\\hncdj\\Documents\\Python_\\Python辅助城市研究\\Urban-Spatial-Data-Analysis-For-Beginners\\4-空间数据分析\\4.2-arcpy\\resource\\PythonWorkflow\\Transportation.gdb\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "import arcpy\n",
    "\n",
    "# 设置工作空间\n",
    "mypath = os.path.join(os.getcwd(), r\"resource\\PythonWorkflow\") # 修改为你的工作目录\n",
    "gdb = \"Transportation.gdb\"\n",
    "\n",
    "arcpy.env.workspace = os.path.join(mypath, gdb)\n",
    "\n",
    "print(arcpy.env.workspace)"
   ],
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   "execution_count": 27,
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     "text": [
      "10 ['bus_lines', 'boundary', 'street_lights', 'roads', 'traffic_analysis_zones', 'bike_routes', 'bike_racks', 'parking_zones', 'intersections', 'station_entrances']\n"
     ]
    },
    {
     "data": {
      "text/plain": "<SpatialReference object at 0x216df017c10[0x216df011250]>",
      "text/html": "<style>table td#td0  {font-weight: bold}</style><table class=\"notebook\"><colgroup><col style=\"width:45%\"></col><col style=\"width:55%\"></col></colgroup><tr><td id = \"td0\" title=\"name (投影坐标系)\">name (投影坐标系)</td><td title=\"NAD_1983_StatePlane_Maryland_FIPS_1900_Feet\">NAD_1983_StatePlane_Maryland_FIPS_1900_Feet</td></tr><tr><td id = \"td0\" title=\"factoryCode (WKID)\">factoryCode (WKID)</td><td title=\"2248\">2248</td></tr><tr><td id = \"td0\" title=\"linearUnitName (线性单位)\">linearUnitName (线性单位)</td><td title=\"Foot_US\">Foot_US</td></tr></table><div class=\"subtitle\">spatialReference.GCS</div><style>table td#td0  {font-weight: bold}</style><table class=\"notebook\"><colgroup><col style=\"width:45%\"></col><col style=\"width:55%\"></col></colgroup><tr><td id = \"td0\" title=\"name (地理坐标系)\">name (地理坐标系)</td><td title=\"GCS_North_American_1983\">GCS_North_American_1983</td></tr><tr><td id = \"td0\" title=\"factoryCode (WKID)\">factoryCode (WKID)</td><td title=\"4269\">4269</td></tr><tr><td id = \"td0\" title=\"angularUnitName (角度单位)\">angularUnitName (角度单位)</td><td title=\"Degree\">Degree</td></tr><tr><td id = \"td0\" title=\"datumName (基准面)\">datumName (基准面)</td><td title=\"D_North_American_1983\">D_North_American_1983</td></tr></table>"
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   ],
   "source": [
    "# 我们先检查一个要素类的投影坐标系\n",
    "fcs = arcpy.ListFeatureClasses() # 获取所有要素类\n",
    "print(len(fcs), fcs)\n",
    "\n",
    "desc = arcpy.da.Describe(fcs[0])  # 获取要素类的描述信息\n",
    "sr = desc[\"spatialReference\"]  # 获取要素类的空间参考\n",
    "sr"
   ],
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   "source": [
    "fcs是要素类名称的 Python 列表。 列表使用方括号括起来，而要素类名称为 Python 字符串，使用逗号分隔。\n",
    "sr是空间参考的 Python 字典。 字典使用花括号括起来，而键和值之间使用冒号分隔。 例如，键“name”对应于空间参考的名称，而键“factoryCode”对应于空间参考的WKID代码。"
   ],
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   "cell_type": "code",
   "execution_count": 28,
   "outputs": [
    {
     "name": "stdout",
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     "text": [
      "bus_lines : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet\n",
      "boundary : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet\n",
      "street_lights : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet\n",
      "roads : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet\n",
      "traffic_analysis_zones : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet\n",
      "bike_routes : GCS_WGS_1984\n",
      "bike_racks : GCS_WGS_1984\n",
      "parking_zones : GCS_WGS_1984\n",
      "intersections : GCS_WGS_1984\n",
      "station_entrances : GCS_WGS_1984\n"
     ]
    }
   ],
   "source": [
    "# 我们检查所有的要素类的坐标系\n",
    "fcs = arcpy.ListFeatureClasses() # 获取所有要素类\n",
    "\n",
    "# 遍历所有要素类\n",
    "for fc in fcs:\n",
    "    desc = arcpy.da.Describe(fc) # 获取要素类的描述信息\n",
    "    sr = desc[\"spatialReference\"] # 获取要素类的空间参考\n",
    "\n",
    "    print(fc + \" : \" + sr.name ) # 打印要素类名称和空间参考名称\n",
    "    "
   ],
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   "source": [
    "这些要素类中有一半在一个坐标系中，另一半在另一个坐标系中。下一步就是将`GCS_WGS_1984`的所有要素类投影到一个坐标系中。但是，在此之前我们创建一个新的地理数据库用于储存投影后的要素。"
   ],
   "metadata": {
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   },
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  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "outputs": [],
   "source": [
    "# 创建新的地理数据库\n",
    "new_gdb = \"Metro_Transport.gdb\"\n",
    "\n",
    "arcpy.env.overwriteOutput = True # 允许覆盖输出\n",
    "\n",
    "new_gdb_path = arcpy.CreateFileGDB_management(mypath, new_gdb) # 创建新的地理数据库 返回值为新的地理数据库的对象, 可以作为工作空间引用"
   ],
   "metadata": {
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    "ExecuteTime": {
     "end_time": "2023-08-24T08:40:09.636559200Z",
     "start_time": "2023-08-24T08:40:09.393181800Z"
    }
   },
   "id": "fc69c8ee7a3daadb"
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "数据库已存在\n"
     ]
    }
   ],
   "source": [
    "# 可以加一个数据库判断 避免重复操作 也可以用于检查数据库是否存在\n",
    "# 判断数据库是否存在，如果不存在则创建\n",
    "if not arcpy.Exists(os.path.join(mypath, new_gdb)):\n",
    "    arcpy.CreateFileGDB_management(mypath, new_gdb) # 创建新的地理数据库\n",
    "else:\n",
    "    print(\"数据库已存在\")\n"
   ],
   "metadata": {
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     "end_time": "2023-08-24T08:40:12.049361600Z",
     "start_time": "2023-08-24T08:40:11.963805700Z"
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   "cell_type": "markdown",
   "source": [
    "接下来我们进行投影操作，投影后的要素类存储在新的地理数据库中。"
   ],
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   "cell_type": "markdown",
   "source": [
    "首先确定我们想要投影到的坐标系，本次会使用roads要素类的坐标系，roads : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet。使用坐标系的名字会很冗长，我们会使用WKID代码来代替坐标系的名字，WKID代码是唯一的，可以代表坐标系。\n",
    "> 国内一般使用CGCS2000坐标系，也有使用百度和高德的坐标系的，具体使用哪个坐标系需要根据实际情况而定。\n",
    "\n",
    "另一种设置坐标系的方法是提供现有要素类的路径，例如，提供 Transportation.gdb 地理数据库中 roads 要素类的路径。"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "c6cfd7f325775005"
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "outputs": [
    {
     "data": {
      "text/plain": "2248"
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 查询roads坐标系NAD_1983_StatePlane_Maryland_FIPS_1900_Feet的WKID代码\n",
    "desc = arcpy.da.Describe(\"roads\") \n",
    "sr = desc[\"spatialReference\"]  # 获取要素类的空间参考\n",
    "sr.factoryCode # 获取坐标系的WKID代码"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-24T08:37:34.904892800Z",
     "start_time": "2023-08-24T08:37:34.608349700Z"
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   "id": "3a0de92aee6dd67e"
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  {
   "cell_type": "code",
   "execution_count": 38,
   "outputs": [],
   "source": [
    "# 将WKID代码储存到变量\n",
    "out_wkid = sr.factoryCode"
   ],
   "metadata": {
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     "end_time": "2023-08-24T08:39:25.665605800Z",
     "start_time": "2023-08-24T08:39:25.642046200Z"
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   "id": "353db784fc0bbc11"
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   "metadata": {
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  {
   "cell_type": "code",
   "execution_count": 42,
   "outputs": [
    {
     "data": {
      "text/plain": "<Result 'C:\\\\Users\\\\hncdj\\\\Documents\\\\Python_\\\\Python辅助城市研究\\\\Urban-Spatial-Data-Analysis-For-Beginners\\\\4-空间数据分析\\\\4.2-arcpy\\\\resource\\\\PythonWorkflow\\\\Metro_Transport.gdb\\\\Metro_Network'>",
      "text/html": "<div class='gpresult'><h2>Messages</h2><div id='messages' data-messages='[\"开始时间: 2023年8月24日 16:41:32\",\"运行 成功，结束时间： 2023年8月24日 16:41:33 (历时: 0.53 秒)\"]' data-show='true'><div id = 'default' /></div></div>"
     },
     "execution_count": 42,
     "metadata": {},
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    }
   ],
   "source": [
    "# 同样我们创建一个空的要素数据集\n",
    "fds = \"Metro_Network\" # 要素数据集名称\n",
    "arcpy.CreateFeatureDataset_management(new_gdb_path, fds, out_wkid)  # 创建要素数据集"
   ],
   "metadata": {
    "collapsed": false,
    "ExecuteTime": {
     "end_time": "2023-08-24T08:41:33.224371200Z",
     "start_time": "2023-08-24T08:41:32.532699400Z"
    }
   },
   "id": "1beebd6898d19f57"
  },
  {
   "cell_type": "markdown",
   "source": [
    "根据条件复制或投影要素类到新要素集\n",
    "复制要素工具`arcpy.CopyFeatures_management`和投影工具`arcpy.Project_management`都会使用一个输入要素类并生成一个输出要素类。 虽然要素类的名称可以保持相同，但输出的路径将有所不同，因为新的要素类将位于新的地理数据库中。"
   ],
   "metadata": {
    "collapsed": false
   },
   "id": "70d1fb1c1a97af82"
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bus_lines : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet : 复制成功\n",
      "boundary : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet : 复制成功\n",
      "street_lights : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet : 复制成功\n",
      "roads : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet : 复制成功\n",
      "traffic_analysis_zones : NAD_1983_StatePlane_Maryland_FIPS_1900_Feet : 复制成功\n",
      "bike_routes : GCS_WGS_1984 : 投影成功\n",
      "bike_racks : GCS_WGS_1984 : 投影成功\n",
      "parking_zones : GCS_WGS_1984 : 投影成功\n",
      "intersections : GCS_WGS_1984 : 投影成功\n",
      "station_entrances : GCS_WGS_1984 : 投影成功\n"
     ]
    }
   ],
   "source": [
    "fcs = arcpy.ListFeatureClasses() # 获取所有要素类\n",
    "\n",
    "# 遍历所有要素类\n",
    "for fc in fcs:\n",
    "    desc = arcpy.da.Describe(fc) # 获取要素类的描述信息\n",
    "    sr = desc[\"spatialReference\"] # 获取要素类的空间参考\n",
    "\n",
    "    new_fc = os.path.join(mypath, new_gdb, fds, fc) # 新要素类的路径\n",
    "    \n",
    "    if sr.factoryCode == out_wkid: # 如果要素类的WKID代码为2248 则进行复制 否则投影\n",
    "        arcpy.CopyFeatures_management(fc, new_fc) # 复制\n",
    "        print(fc + \" : \" + sr.factoryCode + \" : \" + \"复制成功\")\n",
    "    else:\n",
    "        arcpy.Project_management(fc, new_fc, out_wkid) # 投影\n",
    "        print(fc + \" : \" + sr.factoryCode + \" : \" + \"投影成功\")\n"
   ],
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     "end_time": "2023-08-24T08:50:37.626951100Z",
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   },
   "id": "ea925c49ddba2070"
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  {
   "cell_type": "markdown",
   "source": [
    "以下为完整的脚本："
   ],
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  {
   "cell_type": "code",
   "execution_count": null,
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   "id": "6a43727d55294725"
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   "cell_type": "code",
   "execution_count": null,
   "outputs": [],
   "source": [],
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