在众多NoSQL数据库，MongoDB是一个优秀的产品。其官方介绍如下： MongoDB (from "humongous") is a scalable, high-performance, open source, document-oriented database.

看起来，十分诱人！值得说明的是，MongoDB的document是以BSON（Binary JSON）格式存储的，完全支持Schema Free。这对地理空间数据是十分友好的。因为有著名的GeoJSON可供使用。另外OGR库也支持将Geometry类型导出为JSON格式。

本文将尝试使用OGR库把Shapefile导入到MongoDB存储，然后建立空间索引，进行空间查询。

著名的Foursquare使用了MongoDB数据库。

MongoDB+Python+Pymongo+GDAL for Python

关于MongoDB和Python安装，本文不做介绍。在继续本文之前，请先启动你的MongoDB服务器。本文默认采用如下服务器参数： Server:localhost Post:27017 Database Name:gisdb

这里我直接提供代码，代码中已经有比较详尽的注释了。代码基本源于“引文1”，只是做了些改动，将MongoDB的Geometry的存储格式由wkt改成json。你可直接复制并运行下面的代码，当然需要修改一下Shapefile路径和MongoDB服务器相关参数。

import os import sys import json from pymongo import json_util from pymongo.connection import Connection from progressbar import ProgressBar from osgeo import ogr

def shp2mongodb(shape_path, mongodb_server, mongodb_port, mongodb_db, mongodb_collection, append, query_filter):         """Convert a shapefile to a mongodb collection"""         print ‘Converting a shapefile to a mongodb collection ‘         driver = ogr.GetDriverByName(‘ESRI Shapefile’)         print ‘Opening the shapefile %s…’ % shape_path         ds = driver.Open(shape_path, 0)         if ds is None:                 print ‘Can not open’, ds                 sys.exit(1)         lyr = ds.GetLayer()         totfeats = lyr.GetFeatureCount()         lyr.SetAttributeFilter(query_filter)         print ‘Starting to load %s of %s features in shapefile %s to MongoDB…’ % (lyr.GetFeatureCount(), totfeats, lyr.GetName())         print ‘Opening MongoDB connection to server %s:%i…’ % (mongodb_server, mongodb_port)         connection = Connection(mongodb_server, mongodb_port)         print ‘Getting database %s’ % mongodb_db         db = connection[mongodb_db]         print ‘Getting the collection %s’ % mongodb_collection         collection = db[mongodb_collection]         if append == False:                 print ‘Removing features from the collection…’                 collection.remove({})         print ‘Starting loading features…’         # define the progressbar         pbar = ProgressBar(maxval=lyr.GetFeatureCount()).start()         k=0         # iterate the features and access its attributes (including geometry) to store them in MongoDb         feat = lyr.GetNextFeature()         while feat:                 mongofeat = {}                 geom = feat.GetGeometryRef()                 mongogeom = geom.ExportToJson()                 # store the geometry data with json format                 mongofeat['geom'] = json.loads(mongogeom,object_hook=json_util.object_hook)                # iterate the feature’s  fields to get its values and store them in MongoDb                 feat_defn = lyr.GetLayerDefn()                 for i in range(feat_defn.GetFieldCount()):                         value = feat.GetField(i)                         if isinstance(value, str):                                 value = unicode(value, "gb2312")                         field = feat.GetFieldDefnRef(i)                         fieldname = field.GetName()                         mongofeat[fieldname] = value                 # insert the feature in the collection                 collection.insert(mongofeat)                 feat.Destroy()                 feat = lyr.GetNextFeature()                 k = k + 1                 pbar.update(k)         pbar.finish()         print ‘%s features loaded in MongoDb from shapefile.’ % lyr.GetFeatureCount()                 input_shape = ‘/home/evan/data/map/res4_4m/XianCh_point.shp’ mongodb_server = ‘localhost’ mongodb_port = 27017 mongodb_db = ‘gisdb’ mongodb_collection = ‘xqpoint’ filter = ”

print ‘Importing data to mongodb…’ shp2mongodb(input_shape, mongodb_server, mongodb_port, mongodb_db, mongodb_collection, False, filter)

在MongoDB的Shell中执行： >db.xqpoint.findOne() 结果如下：

{     "_id" : ObjectId("4dc82e7f7de36a5ceb000000"),     "PERIMETER" : 0,     "NAME" : "漠河县",     "PYNAME" : "Mohe Xian",     "AREA" : 0,     "ADCODE93" : 232723,     "CNTYPT_ID" : 31,     "CNTYPT_" : 1,     "geom" : {         "type" : "Point",         "coordinates" : [             122.53233,             52.968872         ]     },     "ID" : 1031,     "PN" : 1,     "CLASS" : "AI" } 

这便是一个document，使用JSON格式，一目了然。其中的"geom"即为Geometry类型的数据，即地理空间数据，也是采用JSON格式存储，这样后续的空间索引与空间查询将十分方便。

MongoDB原生地支持了空间索引与空间查询，这一点比PostgreSQL方便，不再需要使用PostGIS进行空间扩展了。至于性能，我还没测试，在此不敢妄加评论。

>db.xqpoint.ensureIndex({‘geom.coordinates’:’2d’})

是不是十分简单？其它参数及用法请自行查看MongoDB手册。

>db.xqpoint.find({"geom.coordinates":[122.53233,52.968872]})

即可查询到上述“莫河县”这个点。当然，像这种精确查询，实际应用并不多。实际应用的空间查询大多为范围查询。MongoDB支持邻域查询（$near），和范围查询（$within）。

>db.xqpoint.find({"geom.coordinates":{$near:[122,52]}}) 上述查询语句查询点[122,52]附近的点，MongoDB默认返回附近的100个点，并按距离排序。你也可以用limit()指定返回的结果数量，如：>db.xqpoint.find({"geom.coordinates":{$near:[122,52]}}).limit(5)

另外，你也可以指定一个最大距离，只查询这个距离内的点。 >db.xqpoint.find({"geom.coordinates":{$near:[122,52],$maxDistance:5}}).limit(5)

MongoDB的find()方法可很方便的进行查询，同时MongoDB也提供了geoNear命令，用于邻域查询。 >db.runCommand({geoNear:"xqpoint",near:[122,56],num:2}) 上述语句用于查询[122,56]点附近的点，并只返回2个点。结果如下：

{     "ns" : "gisdb.xqpoint",     "near" : "1110011000111101111100010000011000111101111100010000",     "results" : [         {             "dis" : 3.077515616588727,             "obj" : {                 "_id" : ObjectId("4dc82e7f7de36a5ceb000000"),                 "PERIMETER" : 0,                 "NAME" : "漠河县",                 "PYNAME" : "Mohe Xian",                 "AREA" : 0,                 "ADCODE93" : 232723,                 "CNTYPT_ID" : 31,                 "CNTYPT_" : 1,                 "geom" : {                     "type" : "Point",                     "coordinates" : [                         122.53233,                         52.968872                     ]                 },                 "ID" : 1031,                 "PN" : 1,                 "CLASS" : "AI"             }         },         {             "dis" : 4.551319677334594,             "obj" : {                 "_id" : ObjectId("4dc82e7f7de36a5ceb000001"),                 "PERIMETER" : 0,                 "NAME" : "塔河县",                 "PYNAME" : "Tahe Xian",                 "AREA" : 0,                 "ADCODE93" : 232722,                 "CNTYPT_ID" : 66,                 "CNTYPT_" : 2,                 "geom" : {                     "type" : "Point",                     "coordinates" : [                         124.7058,                         52.340332                     ]                 },                 "ID" : 1059,                 "PN" : 1,                 "CLASS" : "AI"             }         }     ],     "stats" : {         "time" : 0,         "btreelocs" : 85,         "nscanned" : 85,         "objectsLoaded" : 4,         "avgDistance" : 3.814417646961661,         "maxDistance" : 4.551319677334594     },     "ok" : 1 }

当然，我们也可附加条件查询条件，如查询[122,56]附近的且"PYNAME"为"Tahe Xian"的点： >db.runCommand({geoNear:"xqpoint",near:[122,56],num:2,query:{"PYNAME":"Tahe Xian"}) 返回结果如下：

{     "ns" : "gisdb.xqpoint",     "near" : "1110011000111101111100010000011000111101111100010000",     "results" : [         {             "dis" : 4.551319677334594,             "obj" : {                 "_id" : ObjectId("4dc82e7f7de36a5ceb000001"),                 "PERIMETER" : 0,                 "NAME" : "塔河县",                 "PYNAME" : "Tahe Xian",                 "AREA" : 0,                 "ADCODE93" : 232722,                 "CNTYPT_ID" : 66,                 "CNTYPT_" : 2,                 "geom" : {                     "type" : "Point",                     "coordinates" : [                         124.7058,                         52.340332                     ]                 },                 "ID" : 1059,                 "PN" : 1,                 "CLASS" : "AI"             }         }     ],     "stats" : {         "time" : 45,         "btreelocs" : 2095,         "nscanned" : 2096,         "objectsLoaded" : 2096,         "avgDistance" : 4.551319677334594,         "maxDistance" : 4.551319677334594     },     "ok" : 1 }

MongoDB的$within操作符支持的形状有$box（矩形）,$center（圆形）,$polygon（多边形，包括凹多边形和凸多边形）。所有的范围查询，默认是包含边界的。

查询一个矩形范围，需要指定矩形的左下角和右上角两个坐标点，如下： > box = [[80,40],[100,50]] > db.xqpoint.find({"geom.coordinates":{$within:{$box:box}}})

查询一个圆形范围，需要指定圆心坐标和半径，如下： > center = [80,44] > radius =5 > db.xqpoint.find({"geom.coordinates":{$within:{$center:[center,radius]}}})

查询一个多边形范围，需要指定多边形的各个顶点，可以通过一个顶点数组或一系列点对象指定。其中，最后一个点是默认与第一个点连接的。如下： > polygon1 = [[75,35],[80,35],[80,45],[60,40]] > db.xqpoint.find({"geom.coordinates":{$within:{$polygon:polygon1}}}) 或者 > polygon2 = ｛a:{75,35},b:{80,35},c:{80,45},d:{60,40}} > db.xqpoint.find({"geom.coordinates":{$within:{$polygon:polygon2}}})

注意：MongoDB 1.9及以上版本才支持多边形范围查询。

P.S. MongoDB还支持复合索引，球面模型（可简单理解为投影吧），多位置文档（Multi-location Documents，即一个文档中包括多个Geometry），可参见“引文2”或MongoDB手册。

引文1：http://www.paolocorti.net/2009/12/06/using-mongodb-to-store-geographic-data/ 引文2：http://www.mongodb.org/display/DOCS/Geospatial+Indexing