使用R语言优雅的获取任意区域的POI，道路，河流等数据

POI是“Polnt of Information”的缩写，中文可以翻译为“信息点”。是地图上任何非地理意义的有意义的点，如商店，酒吧，加油站，医院，车站等。POI，道路网，河流等是我们日常研究中经常需要使用的数据。

目前这些数据的获取都有一定的难度，有的下载需要付费，使用代码爬取又需要编写复杂的代码。有时很难成功获得我们想要的数据。

OSM地图数据是指来自OpenStreetMap（开放街道地图）项目的地理信息数据。OpenStreetMap是一个由全球志愿者共同维护的开源地图项目，OSM地图数据的特点包括开放性和可编辑性，这意味着任何人都可以贡献和改进地图数据。这些数据涵盖了道路、建筑、水系、地形等多种地理要素，具有丰富的地理信息和细节。先看看效果：

今天使用R语言调用的就是OSM的各类数据，R语言中的osmdata 包提供有关全球各种空间属性和对象的空间数据。available_features（） 函数可用于获取 OSM 中已识别特征的列表。可用功能的列表可以在 OSM wiki 中找到。

##   [1] "4wd_only"                    "abandoned"                  
##   [3] "abutters"                    "access"                     
##   [5] "addr"                        "addr:city"                  
##   [7] "addr:conscriptionnumber"     "addr:country"               
##   [9] "addr:county"                 "addr:district"              
##  [11] "addr:flats"                  "addr:full"                  
##  [13] "addr:hamlet"                 "addr:housename"             
##  [15] "addr:housenumber"            "addr:inclusion"             
##  [17] "addr:interpolation"          "addr:place"                 
##  [19] "addr:postbox"                "addr:postcode"              
##  [21] "addr:province"               "addr:state"                 
##  [23] "addr:street"                 "addr:subdistrict"           
##  [25] "addr:suburb"                 "addr:unit"                  
##  [27] "admin_level"                 "aeroway"                    
##  [29] "agricultural"                "alt_name"                   
##  [31] "amenity"                     "area"                       
##  [33] "atv"                         "backward"                   
##  [35] "barrier"                     "basin"                      
##  [37] "bdouble"                     "bicycle"                    
##  [39] "bicycle_road"                "biergarten"                 
##  [41] "boat"                        "border_type"                
##  [43] "boundary"                    "brand"                      
##  [45] "bridge"                      "building"                   
##  [47] "building:colour"             "building:fireproof"         
##  [49] "building:flats"              "building:levels"            
##  [51] "building:material"           "building:min_level"         
##  [53] "building:part"               "building:soft_storey"       
##  [55] "bus_bay"                     "busway"                     
##  [57] "capacity"                    "castle_type"                
##  [59] "change"                      "charge"                     
##  [61] "clothes"                     "construction"               
##  [63] "construction#Railways"       "covered"                    
##  [65] "craft"                       "crossing"                   
##  [67] "crossing:island"             "cuisine"                    
##  [69] "cutting"                     "cycleway"                   
##  [71] "denomination"                "destination"                
##  [73] "diet"                        "direction"                  
##  [75] "dispensing"                  "disused"                    
##  [77] "drinking_water"              "drive_in"                   
##  [79] "drive_through"               "ele"                        
##  [81] "electric_bicycle"            "electrified"                
##  [83] "embankment"                  "embedded_rails"             
##  [85] "emergency"                   "end_date"                   
##  [87] "entrance"                    "est_width"                  
##  [89] "fee"                         "female"                     
##  [91] "fire_object:type"            "fire_operator"              
##  [93] "fire_rank"                   "foot"                       
##  [95] "footway"                     "ford"                       
##  [97] "forestry"                    "forward"                    
##  [99] "frequency"                   "fuel"                       
## [101] "gauge"                       "golf_cart"                  
## [103] "goods"                       "hazard"                     
## [105] "hazmat"                      "healthcare"                 
## [107] "healthcare:counselling"      "healthcare:speciality"      
## [109] "height"                      "hgv"                        
## [111] "highway"                     "historic"                   
## [113] "horse"                       "hot_water"                  
## [115] "ice_road"                    "incline"                    
## [117] "industrial"                  "inline_skates"              
## [119] "inscription"                 "int_name"                   
## [121] "internet_access"             "junction"                   
## [123] "kerb"                        "landuse"                    
## [125] "lanes"                       "lanes:bus"                  
## [127] "lanes:psv"                   "layer"                      
## [129] "leaf_cycle"                  "leaf_type"                  
## [131] "leisure"                     "lhv"                        
## [133] "lit"                         "loc_name"                   
## [135] "location"                    "male"                       
## [137] "man_made"                    "max_age"                    
## [139] "max_level"                   "maxaxleload"                
## [141] "maxheight"                   "maxlength"                  
## [143] "maxspeed"                    "maxstay"                    
## [145] "maxweight"                   "maxwidth"                   
## [147] "military"                    "min_age"                    
## [149] "min_level"                   "minspeed"                   
## [151] "mofa"                        "moped"                      
## [153] "motor_vehicle"               "motorboat"                  
## [155] "motorcar"                    "motorcycle"                 
## [157] "motorroad"                   "mountain_pass"              
## [159] "mtb:description"             "mtb:scale"                  
## [161] "name"                        "name:left"                  
## [163] "name:right"                  "name_1"                     
## [165] "name_2"                      "narrow"                     
## [167] "nat_name"                    "natural"                    
## [169] "noexit"                      "non_existent_levels"        
## [171] "nudism"                      "office"                     
## [173] "official_name"               "old_name"                   
## [175] "oneway"                      "opening_hours"              
## [177] "opening_hours:drive_through" "operator"                   
## [179] "orientation"                 "oven"                       
## [181] "overtaking"                  "parking"                    
## [183] "parking:condition"           "parking:lane"               
## [185] "passing_places"              "place"                      
## [187] "power"                       "power_supply"               
## [189] "priority"                    "priority_road"              
## [191] "produce"                     "proposed"                   
## [193] "protected_area"              "psv"                        
## [195] "public_transport"            "railway"                    
## [197] "railway:preserved"           "railway:track_ref"          
## [199] "recycling_type"              "ref"                        
## [201] "reg_name"                    "religion"                   
## [203] "rental"                      "residential"                
## [205] "roadtrain"                   "route"                      
## [207] "sac_scale"                   "sauna"                      
## [209] "service"                     "service_times"              
## [211] "shelter_type"                "shop"                       
## [213] "short_name"                  "shower"                     
## [215] "sidewalk"                    "site"                       
## [217] "ski"                         "smoothness"                 
## [219] "social_facility"             "sorting_name"               
## [221] "speed_pedelec"               "start_date"                 
## [223] "step_count"                  "substation"                 
## [225] "surface"                     "tactile_paving"             
## [227] "tank"                        "tidal"                      
## [229] "toilets"                     "toilets:wheelchair"         
## [231] "toll"                        "topless"                    
## [233] "tourism"                     "tracks"                     
## [235] "tracktype"                   "traffic_calming"            
## [237] "traffic_sign"                "trail_visibility"           
## [239] "trailblazed"                 "trailblazed:visibility"     
## [241] "tunnel"                      "turn"                       
## [243] "type"                        "unisex"                     
## [245] "usage"                       "vehicle"                    
## [247] "vending"                     "voltage"                    
## [249] "water"                       "wheelchair"                 
## [251] "wholesale"                   "width"                      
## [253] "winter_road"                 "wood"
这些就是你可以调用的OSM数据种类

我们今天想使用的是某地区的医院，道路，河流等数据，它们分别属于"amenity"，"highway"，"waterway"。

下面我们开始具体代码：

#导入OSMdata和处理矢量数据的包
library(osmdata)
library(sf)

下一步就是创建 osmdata 查询，第一步是定义我们想要包含在查询中的地理区域，这通常是一个城市，osmdata提供了查询函数，十分方便。

nc_bb <- getbb("nanchang")
nc_bb
#这会返回南昌市的边界范围
##        min      max
## x 115.43870 116.56548
## y 28.15784 29.1223

确认没问题，我们就可以直接查询南昌市境内的所有医院：

nc_hospitals <- nc_bb %>%opq() %>%add_osm_feature(key = "amenity", value = "hospital") %>%osmdata_sf()

然后我们就获得了南昌市医院的数据：nc_hospitals,当然上面的代码由于一些不能科学上网或者服务限制的原因，不出问题的话肯定会出问题。所以下面贴出改进后的代码：

原理一样只是写法变化了，我们直接使用我们本地的南昌市数据

library(osmdata)
library(sf)
zh = st_read("E:/Arcgis 地图资源/市县/市县/南昌市.shp")
bb = st_bbox(zh)
bbnanc_hosiptal <- opq (bbox = c (115.43870,28.15784,116.56548,29.12239 )) %>% # add_osm_feature(key = "amenity", value = "hospital") %>%osmdata_sf ()

我们可以开心的可视化了：osm 数据支持“点”，“线”，“”面多种的数据类型，像医院我们就可以有面数据和点数据。我这里的demo将只展示面的，如果你想要南昌市医院的点数据，可以把“nanc_hosiptal$osm_polygons”改成“nanc_hosiptal$osm_point”

# install.packages("leaflet")
library(leaflet)leaflet() %>%addTiles() %>%addPolygons(data = nanc_hosiptal$osm_polygons,label = nanc_hosiptal$osm_polygons$name)

我们就可以得到：

最后，如果你想要在arcgis,或者QGIS中使用，可以将数据转化成shp格式或者GeoPackage,ESRI Shapefile只支持255列，列名限制为10个字符，文件大小限制为10G，所以这里转成shp保存下来必须要根据这些要求进行处理。因此这里最推荐使用GeoPackage，它是一个轻量级的空间数据库容器，没有奇奇怪怪的限制。它只需要一句话：

nch = nanc_hosiptal$osm_polygons
st_write(nch, "G:/R/nanc_hospital.gpkg")

我们可以打开QGIS看看，如下图，没有任何问题。通过这个方法实际上我们可以获取很多有意思的数据，上面提到的数据我们都可以通过这个方法下载。在这里不得不感叹OSM的强大。

今天就到这里结束了，小编也是摸索了很久才给大家带来这篇推文的，如果大家觉得有用希望点赞转发支持一下，我是加拿大一枝黄花，我们下期再见。