class: center, middle, inverse, title-slide # Geocodificación ## 🌎 <br/><br/>con <em>R</em> ### Francisco Goerlich ### Universidad de Valencia e Ivie ### 29/04/2021 --- # Contexto... <img src="./img/01_MonoFRA.jpg" width="100%" style="display: block; margin: auto;" /> <br> Cualquier ejercicio de accesibilidad (geográfica) tiene (al menos) 3 patas: 1. un **origen** por parte de quien accede, 1. una **ruta**, por algún medio de trasporte determinado, y 1. un **destino** al centro donde se accede. <br> -- ¡Empezaremos por el final! La localización de los destinos. ??? Se trata también de ilustrar nuevas fuente de datos y tratamientos alternativos de la información. --- # Preliminares ¿Que entendemos por **geocodificación**? Pues, ¡ponerlo en el mapa! -- .pull-left[ **No** georeferenciado <img src="./img/02_NoGeo.jpg" width="100%" style="display: block; margin: auto;" /> ] -- .pull-right[ **Si** georeferenciado <img src="./img/02_SiGeo.jpg" width="100%" style="display: block; margin: auto;" /> ] -- En un sentido más **restringido** entendemos por **geocodificación** el proceso de convertir **direccione postales** en **coordenadas**. --- ### ¿Donde está el [Ivie](https://www.ivie.es/es_ES/)? _Daniel Balaciart 3, Valencia, 46020, Valencia_ <div id="htmlwidget-f4a3be5138014d8dd297" style="width:100%;height:432px;" class="leaflet html-widget"></div> <script type="application/json" data-for="htmlwidget-f4a3be5138014d8dd297">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"http://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a href=\"http://creativecommons.org/licenses/by-sa/2.0/\">CC-BY-SA<\/a>"}]}],"setView":[[39.4805,-0.3559],18,[]]},"evals":[],"jsHooks":[]}</script> --- ### Alternativamente En el _CRS WGS84_ en **longitud** -0.3559º y **latitud** 39.4805º. <div id="htmlwidget-90c9a26c00d0b29122a2" style="width:100%;height:432px;" class="leaflet html-widget"></div> <script type="application/json" data-for="htmlwidget-90c9a26c00d0b29122a2">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"http://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a href=\"http://creativecommons.org/licenses/by-sa/2.0/\">CC-BY-SA<\/a>"}]},{"method":"addMarkers","args":[39.4805,-0.3559,{"iconUrl":{"data":"https://www.ivie.es/wp-content/uploads/2017/02/logo.jpg","index":0},"iconWidth":45,"iconHeight":25,"iconAnchorX":15,"iconAnchorY":25},null,null,{"interactive":true,"draggable":false,"keyboard":true,"title":"","alt":"","zIndexOffset":0,"opacity":1,"riseOnHover":false,"riseOffset":250},null,null,null,null,null,{"interactive":false,"permanent":false,"direction":"auto","opacity":1,"offset":[0,0],"textsize":"10px","textOnly":false,"className":"","sticky":true},null]}],"setView":[[39.4805,-0.3559],18,[]],"limits":{"lat":[39.4805,39.4805],"lng":[-0.3559,-0.3559]}},"evals":[],"jsHooks":[]}</script> --- # _CRS: Coordinate Reference Systems_ La información geográfica siempre está necesariamente ligada a un **Sistema de Referencia de Coordenadas (_CRS_)**. Hay básicamente 2 grupos de _CRS_. -- .pull-left[ **Geográficos** <img src="./img/vector_lonlat.png" width="80%" style="display: block; margin: auto;" /> ] -- .pull-right[ **Proyectados** <img src="./img/vector_projected.png" width="80%" style="display: block; margin: auto;" /> ] -- **IMPORANTE:** Siempre que trabajemos con información geográfica debemos asegurarnos que **toda la información** está en el **mismo _CRS_**. --- class: inverse, center, middle # Get Started --- # Problema Geocodificar el fichero de Oficinas bancarias de diciembre de 2020 del Banco de España: `Oficinas2020.csv`. .center[22 558 oficinas. **¡_Real data_!**] > _Classroom data are like teddy bears; real data are like a grizzly with salmon blood dripping out its mouth._ — [@JennyBryan] .bg-washed-green.b--dark-green.ba.bw2.br3.shadow-5.ph4.mt4[ Inspecciona el fichero **`Oficinas2020.csv`**. ] -- Debemos tener en cuenta que un proceso de **geocodificación** masiva es, además de engorroso y tedioso,... - ...una operación imprecisa (dada la tecnología actual), y - que habrá que proceder iterativamente. Los geocodificadores son **extremadamente caprichosos**. --- # Estrategia Antes de empezar **diseña una estrategia**: -- 1. Al `data.frame` original (_read only_) añade variables que vayan resumiendo el proceso de geocodificación, incluyendo si la observación ya está o no georeferenciada y suficiente información para poder rastrear el proceso de geocodificación. -- 1. Dedica suficiente tiempo a la **normalización** de las direcciones. -- 1. Controla la precisión espacial de los resultados. ¿Que margen de error espacial es aceptable? -- 1. Utiliza más de un geocodificador, pero fija con antelación el orden en el que serán llamados. -- 1. ¡No lo encontrarás todo! Si no quieres perder observaciones establece una base de datos de coordenadas de respaldo. ¡💩! ¿Existe eso? --- # Anticipa los resultados a almacenar .bg-washed-green.b--dark-green.ba.bw2.br3.shadow-5.ph4.mt3[ Inspecciona el _script_ **`00Oficinas_prepare.R`**. ] ```r Oficinas <- mutate(tvia = normaliza_TVIA(tvia), Municipio = normaliza_ENT(Municipio, change.order = TRUE), ndir = normaliza_dir(tvia, nvia, npoli, Municipio, Provincia, CP), * Longitud = NA_real_, Latitud = NA_real_, * georef = NA_character_, comment = NA_character_, * Fuente = NA_character_, * Comentarios = NA_character_, * CalidadGeocodificacion = NA_character_, * DireccionNormalizada = NA_character_) ``` --- # Direcciones ¡Las direcciones son la pieza clave de una geocodificación exitosa! .bg-washed-green.b--dark-green.ba.bw2.br3.shadow-5.ph4.mt3[ Mira algunas direcciones del fichero **`Oficinas2020.csv`**. ] -- **Normaliza** las direcciones antes de enviarlas al servicio de geocodificación. 💩 -- Es útil pensar que una dirección postal se compone de 6 elementos: 1. **_tvia_**: tipo de vía 1. **_nvia_**: nombre de la vía 1. **_npoli_**: número de policía 1. **_muni_**: municipio 1. **_prov_**: provincia 1. **_codpost_**: Código Postal --- # Direcciones: normalización Cualquiera que sea tu formato original de direcciones: (_i_), ¡consigue los 6 elementos! -aunque sean cadenas vacías-, (_ii_) normaliza cada elemento y (_iii_) genera una dirección normalizada en la forma .center[_tvia_ _nvia_ _npoli_, _muni_, _prov_, _codpost_] ¡Hay **funciones** para todo esto! Se basan en lo que se denomina _regular expressions_. La librería [_stringr_](https://stringr.tidyverse.org/reference/index.html) facilita notablemente el trabajo. ```r *Oficinas <- split_via_BdE(Oficinas$Direccion) %>% select(-direcciones) %>% bind_cols(Oficinas, .) %>% * mutate(tvia = normaliza_TVIA(tvia), * Municipio = normaliza_ENT(Municipio, change.order = TRUE), * ndir = normaliza_dir(tvia, nvia, npoli, Municipio, Provincia, CP)) ``` --- # Direcciones: funciones ```r split_via(via) *split_via_BdE(via) # Especifica para el BdE normaliza_TVIA(TVIA) normaliza_NVIA(NVIA) normaliza_NPOLI(NPOLI) normaliza_ENT(ENT, keep = c("left", "right"), change.order = FALSE) normaliza_CP(CP) normaliza_dir(tvia, nvia, npoli, muni, prov, codpost, CP.last = TRUE) agita_dir(tvia, nvia, npoli, muni, prov, codpost, CP.last = TRUE, nivel, void = TRUE) *normaliza_Entidad(Entidad) # Especifica para el BdE ``` -- .bg-washed-green.b--dark-green.ba.bw2.br3.shadow-5.ph4.mt4[ Experimenta con las direcciones del fichero **`Oficinas2020.csv`**, y construye la dirección normalizada que enviarás al geocodificador. ] --- # Geocodificadores Ya tenemos las direcciones arregladitas, ¿que hacemos con eso? Pues enviarlas a un **servicio** de geocodificación. El mundo de los servicios tiene _pros_ y _cons_, pero en este caso no hay alternativa, ¡dependemos de él! Acceso mediante **API**: librería o programación. -- ### [Cartociudad](https://www.cartociudad.es/portal/) versus [Google](https://cloud.google.com/maps-platform) .pull-left[ **Cartociudad** 1. Cobertura nacional. 1. Servicio gratuito. 1. _CRS_: ETRS89 1. Documentación de la [API](http://www.cartociudad.es/recursos/Documentacion_tecnica/CARTOCIUDAD_ServiciosWeb.pdf). ] .pull-right[ **Google** 1. Cobertura mundial. 1. Servicio de pago. 1. _CRS_: WGS84 1. Documentación de la [API](https://developers.google.com/maps/documentation/geocoding/overview). ] --- # Geocodificadores: funciones - [Cartociudad](https://www.cartociudad.es/portal/) ```r # Geocodificación masiva --> necesita analisis posterior cartociudad_geocode(full_address, version = c("current", "prev"), progress = TRUE) # Geocodificación individual --> incluye regla de decisión geocodificar_cartociudad(direc, poligono = NULL, buffer = NULL, progress = FALSE) ``` - `cartociudad_geocode()`: **geocodificación masiva** sin ningún control de calidad, de forma que este debe ser realizado _a posteriori_ en función de lo que devuelva el servidor. - `geocodificar_cartociudad()`: **geocodificación individual**, pero **incluye una regla de decisión** en función de calidad de la respuesta, `state`, y **permite comprobar si la coordenada devuelta por el servidor cae dentro de un poligono**. .bg-washed-green.b--dark-green.ba.bw2.br3.shadow-5.ph4.mt4[ Experimenta con ambas funciones y una muestra de direcciones normalizadas, `ndir`. ] --- # Geocodificadores: funciones - [Google](https://cloud.google.com/maps-platform) ```r # Geocodificación masiva --> necesita analisis posterior google_geocode(full_address, clave_google = NULL, progress = TRUE) # Geocodificación individual --> incluye regla de decisión geocodificar_google(direc, clave_google = NULL, poligono = NULL, buffer = NULL, progress = FALSE) ``` `google_geocode()` y `geocodificar_google()` son **funciones análogas** a las de Cartociudad con (casi) idénticos parámetros de entrada y el mismo formato de la respuesta, de forma que los resultados de pueden acumular con `bind_rows()`. ¡La principal diferencia es que, dado que Google es un servicio de pago, requieren una API _key_! Entre otras cosas, por esta razón, la geocodificación de las oficinas bancarias primero pasó por Cartociudad y luego por Google. --- # Georeferenciación de Oficinas: _workflow_ - Se realiza un primer paso por [Cartociudad](https://www.cartociudad.es/portal/) utilizando el poligono del municipio como control de calidad espacial —si la coordenada no cae dentro del municipio se desecha— ```r # 01Oficinas_Carto1.R for(i in 1:n){ poly <- Municipios2020 %>% filter(INECodMuni == Oficinas$CodMuni[i]) * g <- geocodificar_cartociudad(Oficinas$ndir[i], poligono = poly) Oficinas$georef[i] <- g$georef Oficinas$comment[i] <- g$comment if (g$georef == 'SI'){ Oficinas$Longitud[i] <- g$lng Oficinas$Latitud[i] <- g$lat Oficinas$Fuente[i] <- if_else(g$version == 'prev', 'Cartociudad. Version antigua.', 'Cartociudad. Version nueva.') Oficinas$Comentarios[i] <- paste0(g$stateMsg, ' (', g$state, '). Lugar: ', g$muni, ' (', g$province, ').') Oficinas$CalidadGeocodificacion[i] <- 'Coordenada en TM.' Oficinas$DireccionNormalizada[i] <- normaliza_dir(g$tip_via, g$address, g$portalNumber, g$muni, g$province, g$postalCode) } } ``` --- # Georeferenciación de Oficinas: _workflow_ - Esta primera pasada georeferenció el 89.4% de las oficinas, 20 163, con lo que nos faltan por georeferenciar el 10.6% de las oficinas, 2 395. - Si en la primera pasada no llegáis al 80% de tasa de éxito, o las direcciones no están bien tratadas o no tienen la suficiente calidad para acometer el proceso. ¡Volvemos a la casilla de salida! - ¡Agita las direcciones y vuelvelas a pasar por Cartociudad! -- ```r # 02Oficinas_Carto2.R Oficinas <- Oficinas %>% * mutate(vdir = agita_dir(tvia, nvia, npoli, Municipio, Provincia, CP, nivel = niv)) ``` - **Agitando** las direcciones conseguimos georeferenciar 583 oficinas más. ¡Ya tenemos el 92.0%!, nos quedan 1 812 oficinas por georeferenciar, el 8.0%. --- # Georeferenciación de Oficinas: _workflow_ - ¿Tiene Google algo que decir? Sí. Normalmente bastante. Las restantes las pasamos por Google, que encuentra 1 702 oficinas. ¡Nos quedan 110! ```r # 03Oficinas_Google1.R for(j in 1:totno.geo){ i <- no.geo[j] poly <- Municipios2020 %>% filter(INECodMuni == Oficinas$CodMuni[i]) * g <- geocodificar_google(Oficinas$ndir[i], clave_google = APIkey, poligono = poly) Oficinas$georef[i] <- g$georef Oficinas$comment[i] <- g$comment if (g$georef == 'SI'){ Oficinas$Longitud[i] <- g$lng Oficinas$Latitud[i] <- g$lat Oficinas$Fuente[i] <- 'Google' Oficinas$Comentarios[i] <- paste0(g$stateMsg, ' (', g$state, '). Lugar: ', g$muni, ' (', g$province, ').') Oficinas$CalidadGeocodificacion[i] <- 'Coordenada en TM.' Oficinas$DireccionNormalizada[i] <- normaliza_dir(g$tip_via, g$address, g$portalNumber, g$muni, g$province, g$postalCode) } } ``` --- # Georeferenciación de Oficinas: _workflow_ - Google tiene la ventaja de que admite POIs (_Points of Interest_), Cartociudad no. - Probamos a añadir la `Entidad` entidad a la dirección. ```r # 04Oficinas_Google2.R # Añadimos la Entidad al principio de la direccion *direccion <- paste0(Oficinas$Entidad[i], ', ', Oficinas$ndir[i]) g <- geocodificar_google(direccion, clave_google = APIkey, poligono = poly) ``` - Lo que encuentra 77 coordenadas adicionales. - ¡Nos quedan 33 oficinas por geocodificar, el 0.15%! - Si podéis prescindir de 33 observaciones de 22 558 os haréis un favor, en caso contrario solo queda hacer una investigación individual, rebajar las exigencias, y/o acudir a la base de datos de coordenadas de respaldo. En este caso las coordenadas de la Capital del Municipio. --- # Oficinas díscolas (1/3) Llegados a este punto podemos dudar, de forma razonable, de todas las fuentes de información: El geocodificador puede no encontrar la dirección, las coordenadas devueltas pueden ser incorrectas, el fichero original puede tener errores -de hecho los tiene, ¡y muchos!—, y/o el fichero de lindes puede ser impreciso. En este caso concreto se procedió de la siguiente forma: - Después de pasar por Cartociudad y Google se examinaron individualmente las oficinas que no se consiguió georeferenciar. - En particular, se vió donde devolvía el geocodificador coordenadas: _Spatial join_ ```r # 05Oficinas_Spatial_join.R g_join <- st_as_sf(OficinasNO, coords = c('Longitud', 'Latitud'), crs = 4258) %>% * st_join(Municipios2020) ``` --- # Oficinas díscolas (2/3) - Se hicieron búsquedas manuales en Google y en los buscadores de las oficinas de cada entidad. - Ello permitió corregir numerosos errores del fichero original del Banco de España^*. - Rebajando la precisión de la coordenada devuelta por Cartociudad, `state = 6`, se geocodificó 1 oficina más. ```r # 06Oficinas_Carto4.R g <- cartociudad_geocode(Oficinas$ndir[i], ver = "current", progress = FALSE) * if (g$state == 6) # Aceptamos la coordenada con menor precisión ``` - Se mantuvo el criterio de que todas las coordenadas debían "caer" en el término municipal de acuerdo con el mapa del IGN. ¡Aún a sabiendas de que el mapa no es perfecto y contiene errores! .footnote[(*) Esto se hizo en iteraciones anteriores del proceso.] --- # Oficinas díscolas (3/3) - Finalmente, para 32 oficinas, se asignó la **coordenada de la Capital del Municipio**^*. Lo que cierra el proceso (iterativo) de georeferenciación. <br/> ---- .footnote[(*) Esta información procede del Nomenclátor geografico del IGN y se comprobó que las coordenadas caen donde deben hacerlo.] -- <br/> - La distribución final del origen de las coordenadas es <img src="./img/03_CoordenadasFuente.jpg" width="40%" style="display: block; margin: auto;" /> --- # Comprobaciones finales Es **importante** realizar **dos comprobaciones finales**: - Comprobar que **todas las coordenadas caen dentro de la precisión espacial fijada** en la estrategia inicial^*. ```r # 07Oficinas_final.R OfiMuni <- Oficinas %>% st_as_sf(coords = c("Longitud", "Latitud"), crs = 4258) %>% * st_intersection(Municipios2020) # Una interseción espacial hace el trabajo sum(OfiMuni$CodMuni != OfiMuni$INECodMuni) ``` .footnote[(*) Si hay excepciones se deberá dejar registro de ello.] -- - Comprobar **duplicados por coordenada**. Muy útil para detectar errores. ```r # 07Oficinas_final.R duplicados <- Oficinas %>% * janitor::get_dupes(CodMuni, Municipio, CodEnt, Entidad, Longitud, Latitud) ``` --- class: inverse, center, middle # ¿Para que sirve todo esto? --- ### Para pintar oficinas en el mapa... <div id="htmlwidget-3cca33afd6acafd9f3fc" style="width:100%;height:432px;" class="leaflet html-widget"></div> <script type="application/json" data-for="htmlwidget-3cca33afd6acafd9f3fc">{"x":{"options":{"crs":{"crsClass":"L.CRS.EPSG3857","code":null,"proj4def":null,"projectedBounds":null,"options":{}}},"calls":[{"method":"addTiles","args":["//{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",null,null,{"minZoom":0,"maxZoom":18,"tileSize":256,"subdomains":"abc","errorTileUrl":"","tms":false,"noWrap":false,"zoomOffset":0,"zoomReverse":false,"opacity":1,"zIndex":1,"detectRetina":false,"attribution":"© <a href=\"http://openstreetmap.org\">OpenStreetMap<\/a> contributors, <a 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