geo

I have tried this example here, which works fine. But how do I focus on another country, i.e. show only germany? source for this example http://scitools.org.uk/cartopy/docs/latest/examples/hurricane_katrina.html exampl of us map http://scitools.org.uk/cartopy/docs/latest/_images/hurricane_katrina_01_00.png I've tried some coordinates on the extend() method, but I didn't manage to get to look it like the US map. Or do I have to modify the shape file? Using the Global Administrative Areas dataset at http://www.gadm.org/country , just download the Germany dataset and use cartopy's shapereader (in

I am having following JSON array of 6 locations. Is there any way sort these based on longitude and latitude where nearby locations come next in the array? [ {"id" : 279, "longitude":79.853239,"latitude":6.912283}, {"id" : 284, "longitude":79.865699,"latitude":6.885697}, {"id" : 13, "longitude":79.851187,"latitude":6.912220}, {"id" : 282, "longitude":79.858904,"latitude":6.871041}, {"id" : 281, "longitude":79.853346,"latitude":6.899757}, {"id" : 16, "longitude":79.854786,"latitude":6.894039} ] Sorting can be started from first item and result should be something like this [ {"id" : 279,

需求 怎么实现一个“附件的人”或者附近的“商店”功能？ 需求分析 可以基于数据库去做，给定一个坐标，以这个坐标为半径r, j使用sql选出近似圆的的元素进行展示。 select id from positions where x > x0-r and x < x0+r and y < y0+r and y> y0-r 但是如果访问量非常大，那数据库就无能为力，幸好Redis提供了GEO模块，直接可以使用。 业界比较通用的地理位置距离排序算法是 GeoHash 算法，Redis 也使用 GeoHash 算法。GeoHash 算法将二维的经纬度数据映射到一维的整数，这样所有的元素都将在挂载到一条线上，距离靠近的二维坐标映射到一维后的点之间距离也会很接近。 GeoHash 算法会继续对这个整数做一次 base32 编码 (0-9,a-z 去掉 a,i,l,o 四个字母) 变成一个字符串。在 Redis 里面，经纬度使用 52 位的整数进行编码，放进了 zset 里面，zset 的 value 是元素的 key，score 是 GeoHash 的 52 位整数值。zset 的 score 虽然是浮点数，但是对于 52 位的整数值，它可以无损存储。 在使用 Redis 进行 Geo 查询时，我们要时刻想到它的内部结构实际上只是一个 zset(skiplist)。通过 zset 的 score

Does anyone know a good GEO Location tutorial for Android. I don’t want to have to get a Google maps API key I just want an Activity that gets the location of a phone and sticks it in a variable that I can then decide what to do with it later. Cheers, Mike. Only for location then Just go through Using GPS to get current location – Android tutorial Find Current Location in Android - GPS Sample Android Location Based Services Application – GPS location Obtaining User Location Location Manager Examples 来源： https://stackoverflow.com/questions/8543763/android-geo-location-tutorial

​Geo类型 Redis3.2.0版本推出 可以将用户给定的地理位置信息存储起来，并对这些信息进行操作 GEOADD key longitude latitude member [longitude latitude member ...] //在使用排序集表示的地理空间索引中添加一个或多个地理空间项，时间复杂度O(log(N)) 127.0.0.1:6379> geoadd key1 121.3996887207031 31.04559326171875 member1 116.3883056640625 39.92889404296875 member2 (integer) 2 GEODIST key member1 member2 [unit] //返回地理空间索引的两个成员之间的距离，时间复杂度O(log(N)) 127.0.0.1:6379> geodist key1 member1 member2 "1086809.8477"//默认单位为m 127.0.0.1:6379> geodist key1 member1 member2 m "1086809.8477" 127.0.0.1:6379> geodist key1 member1 member2 km "1086.8098" 127.0.0.1:6379> geodist key1 member1 member2

I'm currently having problems in creating a schema for the document below. The response from the server always returns the "trk" field values as [Object]. Somehow I have no idea how this should work, as I tried at least all approaches which made sense to me ;-) If this helps, my Mongoose version is 3.6.20 and MongoDB 2.4.7 And before I forget, it would be nice to also set it as Index (2d) Original data: { "_id": ObjectId("51ec4ac3eb7f7c701b000000"), "gpx": { "metadata": { "desc": "Nürburgring VLN-Variante", "country": "de", "isActive": true }, "trk": [ { "lat": 50.3299594, "lng": 6.9393006 },

I'm looking for a way to get HAVERSINE() in BigQuery. For example, how to get the closest weather stations to an arbitrary point? Using standard SQL you can define a SQL function to encapsulate the logic. For example, #standardSQL CREATE TEMP FUNCTION RADIANS(x FLOAT64) AS ( ACOS(-1) * x / 180 ); CREATE TEMP FUNCTION RADIANS_TO_KM(x FLOAT64) AS ( 111.045 * 180 * x / ACOS(-1) ); CREATE TEMP FUNCTION HAVERSINE(lat1 FLOAT64, long1 FLOAT64, lat2 FLOAT64, long2 FLOAT64) AS ( RADIANS_TO_KM( ACOS(COS(RADIANS(lat1)) * COS(RADIANS(lat2)) * COS(RADIANS(long1) - RADIANS(long2)) + SIN(RADIANS(lat1)) * SIN