前言：
当前咱们对“net空间可以干什么”大约比较注意，你们都需要学习一些“net空间可以干什么”的相关知识。那么小编在网上网罗了一些关于“net空间可以干什么””的相关资讯，希望我们能喜欢，咱们一起来学习一下吧！一：我们先准备点数据
采用高德坐标系来拾取点数 （）
注意：要注册成开发者，否则拾取到的经纬度只精确到2位
第一百货 119.647437,29.083693欧景名城 119.639798,29.080843金华网络经济中心 119.644604,29.074617金华市第十五中学 119.652758,29.075029金磐医院 119.642716,29.072554龙腾江南建材市场 119.636579,29.072366宝莲广场 119.644433,29.079568新世纪大厦 119.655548,29.08088泰地世璟园 119.658809,29.073379美兰湖公馆 119.639497,29.06824西京大厦 119.63645,29.081855星月花园 119.635592,29.078292兰溪街商业步行街 119.649325,29.082606阳光城市花园 119.666963,29.082456华园小区 119.666491,29.073604印象城 119.658509,29.070716
地理位置
二：把数据存到mongodb中，便于后面使用
db.mapinfo.insert({"address": "第一百货", "name":"第一百货", "location": {"type": "Point", "coordinates": [119.647437,29.083693]}})db.mapinfo.insert({"address": "欧景名城", "name":"欧景名城", "location": {"type": "Point", "coordinates": [119.639798,29.080843]}})db.mapinfo.insert({"address": "金华网络经济中心", "name":"金华网络经济中心", "location": {"type": "Point", "coordinates": [119.644604,29.074617]}})db.mapinfo.insert({"address": "金华市第十五中学", "name":"金华市第十五中学", "location": {"type": "Point", "coordinates": [119.652758,29.075029]}})db.mapinfo.insert({"address": "金磐医院", "name":"金磐医院", "location": {"type": "Point", "coordinates": [119.642716,29.072554]}})db.mapinfo.insert({"address": "龙腾江南建材市场", "name":"龙腾江南建材市场", "location": {"type": "Point", "coordinates": [119.636579,29.072366]}})db.mapinfo.insert({"address": "宝莲广场", "name":"宝莲广场", "location": {"type": "Point", "coordinates": [119.644433,29.079568]}})db.mapinfo.insert({"address": "新世纪大厦", "name":"新世纪大厦", "location": {"type": "Point", "coordinates": [119.655548,29.08088]}})db.mapinfo.insert({"address": "泰地世璟园", "name":"泰地世璟园", "location": {"type": "Point", "coordinates": [119.658809,29.073379]}})db.mapinfo.insert({"address": "美兰湖公馆", "name":"美兰湖公馆", "location": {"type": "Point", "coordinates": [119.639497,29.06824]}})db.mapinfo.insert({"address": "西京大厦", "name":"西京大厦", "location": {"type": "Point", "coordinates": [119.63645,29.081855]}})db.mapinfo.insert({"address": "星月花园", "name":"星月花园", "location": {"type": "Point", "coordinates": [119.635592,29.078292]}})db.mapinfo.insert({"address": "兰溪街商业步行街", "name":"兰溪街商业步行街", "location": {"type": "Point", "coordinates": [119.649325,29.082606]}})db.mapinfo.insert({"address": "阳光城市花园", "name":"阳光城市花园", "location": {"type": "Point", "coordinates": [119.666963,29.082456]}})db.mapinfo.insert({"address": "华园小区", "name":"华园小区", "location": {"type": "Point", "coordinates": [119.666491,29.073604]}})db.mapinfo.insert({"address": "印象城", "name":"印象城", "location": {"type": "Point", "coordinates": [119.658509,29.070716]}})一定要加索引
mongodb 提供的地图索引有两种，分别是 2d 和 2dsphere。
2d 索引通过二维平面记录点坐标，支持在平面几何中计算距离，而 2dsphere 则支持在球面上进行距离的计算，并且支持 mongodb 的所有地理空间查询方法。
简单的理解，2dsphere 是 2d 的增强版。根据官方推荐，如果你的mongodb版本大于2.6
db.mapinfo.ensureIndex({location : "2dsphere"})数据库数据
假设当前位置在“1921文化创意园” 具体坐标 119.627438,29.078988
当前位置
检索规定半径以内数据（单位为米）
这里取2000米
db.mapinfo.find({location: {$near: {$geometry: {type: "Point", coordinates: [119.627438,29.078988]}, $maxDistance: 2000}}})或者
db.mapinfo.find({location: {$nearSphere: {$geometry: {type: "Point", coordinates: [119.627438,29.078988]}, $maxDistance: 2000}}})查询结果，由近到远
按照离我最近排序，除了使用 $nearSphere 查询外，我们还可以使用 aggregate 来实现。
使用 aggregate 有两个好处。
1.我们在进行排序的后，可以返回两点之间的距离。
2.我们可以进行更为复杂的排序，例如我们可以先根据某个字段进行排序，然后该字段相同的情况下再根据距离排序。
db.mapinfo.aggregate([ { $geoNear: { near: {type: "Point", coordinates: [119.627438,29.078988]}, distanceField: "distance", spherical: true, maxDistance: 15000, query: { address: {$regex:'建材'} } } }, { $skip: 0 }, { $limit: 2 }])aggregate 查询
如果我们希望查询以某个点为中心的圆几公里以内的数据，那你可以如下操作：
地球表面1弧度距离约为6378137米, 0.001弧度距离为6378米
db.mapinfo.find({ "location": { "$geoWithin": { "$centerSphere": [ [ 119.627438,29.078988 ], 0.025 // 单位为弧度 ] } }})以某个点为中心的圆几公里以内的数据
写到这里，其实已经实现了基于MongoDB 空间定位（点）与距离检索
那.net中代码如何实现，看下面具体说明。
编写实体对象 mapinfo
public class mapinfo { /// <summary> /// 主键 /// </summary> [BsonId] [BsonRepresentation(BsonType.ObjectId)] public string id { get; set; } /// <summary> /// 地址 /// </summary> public string address { get; set; } /// <summary> /// 社区名称 /// </summary> public string name { get; set; } /// <summary> /// 距离 /// </summary> public double? distance { get; set; } /// <summary> /// 经纬度坐标 /// </summary> public GeoJsonPoint<GeoJson2DGeographicCoordinates> location { get; set; } }IMapinfoMongoRepository
public interface IMapinfoMongoRepository { public List<Models.MongoEnity.mapinfo> QueryNear(string keywords,int pageIndex, int pageSize, double longitude, double latitude, int maxDistance); }MapinfoMongoRepository
public class MapinfoMongoRepository : IMapinfoMongoRepository { private readonly ILogger<MapinfoMongoRepository> _logger; private readonly IMongoDbGenericHelper<Models.MongoEnity.mapinfo> _mapinfoHelper; public MapinfoMongoRepository(ILogger<MapinfoMongoRepository> logger, IMongoDbGenericHelper<Models.MongoEnity.mapinfo> mapinfoHelper) { _logger = logger; _mapinfoHelper = mapinfoHelper; } /// <summary> /// 查询附近的信息 /// </summary> /// <param name="keywords"></param> /// <param name="pageIndex"></param> /// <param name="pageSize"></param> /// <param name="longitude"></param> /// <param name="latitude"></param> /// <param name="maxDistance"></param> /// <returns></returns> public List<Models.MongoEnity.mapinfo> QueryNear(string keywords, int pageIndex, int pageSize, double longitude, double latitude, int maxDistance) { IList<IPipelineStageDefinition> stages = new List<IPipelineStageDefinition>(); string temp1 = ""; if (!string.IsNullOrEmpty(keywords)) { temp1 = "{\"$geoNear\": {\"near\": {\"type\": \"Point\",\"coordinates\": [" + longitude + "," + latitude + "]},\"distanceField\": \"distance\",\"spherical\": true,\"maxDistance\": " + maxDistance + ",query:{\"address\": { $regex: '" + keywords + "' }}}}"; } else { temp1 = "{\"$geoNear\": {\"near\": {\"type\": \"Point\",\"coordinates\": [" + longitude + "," + latitude + "]},\"distanceField\": \"distance\",\"spherical\": true,\"maxDistance\": " + maxDistance + "}}"; } PipelineStageDefinition<Models.MongoEnity.mapinfo, Models.MongoEnity.mapinfo> stage1 = new JsonPipelineStageDefinition<Models.MongoEnity.mapinfo, Models.MongoEnity.mapinfo>(temp1); stages.Add(stage1); string temp2 = "{$skip:" + (pageIndex - 1) * pageSize + "}"; PipelineStageDefinition<Models.MongoEnity.mapinfo, Models.MongoEnity.mapinfo> stage2 = new JsonPipelineStageDefinition<Models.MongoEnity.mapinfo, Models.MongoEnity.mapinfo>(temp2); stages.Add(stage2); string temp3 = "{$limit:" + pageSize + "}"; PipelineStageDefinition<Models.MongoEnity.mapinfo, Models.MongoEnity.mapinfo> stage3 = new JsonPipelineStageDefinition<Models.MongoEnity.mapinfo, Models.MongoEnity.mapinfo>(temp3); stages.Add(stage3); PipelineDefinition<Models.MongoEnity.mapinfo, Models.MongoEnity.mapinfo> pipeline = new PipelineStagePipelineDefinition<Models.MongoEnity.mapinfo, Models.MongoEnity.mapinfo>(stages); //聚合结果 List<Models.MongoEnity.mapinfo> result = _mapinfoHelper.GetCollection(nameof(Models.MongoEnity.mapinfo)).Aggregate(pipeline).ToList(); return result; } }Action
[HttpGet] public IActionResult TestQueryNear() { var data = _mapinfoMongoRepository.QueryNear(keywords: string.Empty, pageIndex: 1, pageSize: 2, longitude: 119.627438, latitude: 29.078988, maxDistance: 15000); return Ok(data); }查询结果：
查询结果
查询结果 distance单位是米
[ { "id": "64ae31fe6412e519c80f4b3d", "address": "星月花园", "name": "星月花园", "distance": 797.0567583679608, "location": { "Coordinates": { "Values": [ 119.635592, 29.078292 ], "Longitude": 119.635592, "Latitude": 29.078292 }, "Type": 7, "BoundingBox": null, "CoordinateReferenceSystem": null, "ExtraMembers": null } }, { "id": "64ae31fe6412e519c80f4b3c", "address": "西京大厦", "name": "西京大厦", "distance": 933.0219480837998, "location": { "Coordinates": { "Values": [ 119.63645, 29.081855 ], "Longitude": 119.63645, "Latitude": 29.081855 }, "Type": 7, "BoundingBox": null, "CoordinateReferenceSystem": null, "ExtraMembers": null } }]知识整理不易，喜欢的朋友家个guanzhu呗
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