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lucas.longour_ird.fr authoredlucas.longour_ird.fr authored
search.json 106.69 KiB
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"title": "Mapping and spatial analyses in R for One Health studies",
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"text": "This manual is tended both for R users wishing to set up spatial data peocessing and for users wishing to use R to carry out the tasks that they usually carry out with GIS. The main steps in the processing of geographic information are covered. Emphasis is placed on the processing of vector data but a part is still dedicated to raster data.\nHow to use the manual\nThe RStudio project containing all the data used in the manual is available here. Once the file is unzipped it is possible to test all the manipulations proposed in the RStudion project.\nContext\nThis manual has been designed from the courses “Géomatique avec R” and “Cartographie avec R” by Timothée Giraud and Hugues Pecout. It has been translated and its examples have been adapted to the geographical distribution of the audience.\n\n\n\n\nCreative Commons License\n\n\nThe online version of this document licensed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0."
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"text": "Historically, 4 packages make it possible to import, manipulate and transform spatial data:\n\nThe package rgdal (Bivand, Keitt, and Rowlingson 2022) which is an interface between R and the GDAL (GDAL/OGR contributors, n.d.) and PROJ (PROJ contributors 2021) libraries allow you to import and export spatial data (shapefiles for example) and also to manage cartographic projections\n\nThe package sp (E. J. Pebesma and Bivand 2005) provides class and methods for vector spatial data in R. It allows displaying background maps, inspectiong an attribute table etc.\n\nThe package rgeos (Bivand and Rundel 2021) gives access to the GEOS spatial operations library and therefore makes classic GIS operations available: calculation of surfaces or perimeters, calculation of distances, spatial aggregations, buffer zones, intersections, etc.\n\nThe package raster (Hijmans 2022a) is dedicated to the import, manipulation and modeling of raster data.\n\nToday, the main developments concerning vector data have moved away from the old 3 (sp, rgdal, rgeos) to rely mainly on the package sf ((E. Pebesma 2018a), (E. Pebesma 2018b)). In this manual we will rely exclusively on this package to manipulate vector data.\nThe packages stars (E. Pebesma 2021) and terra (Hijmans 2022b) come to replace the package raster for processing raster data. We have chosen to use the package here terra for its proximity to the raster."
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"text": "1.2 The package sf\n The package sf was released in late 2016 by Edzer Pebesma (also author of sp). Its goal is to combine the feature of sp, rgeos and rgdal in a single, more ergonomic package. This package offers simple objects (following the simple feature standard) which are easier to manipulate. Particular attention has been paid to the compatibility of the package with the pipe syntax and the operators of the tidyverse.\nsf directly uses the GDAL, GEOS and PROJ libraries.\n\n\n\n\n\nFrom r-spatial.org\n\n\n\n\n\n\nWebsite of package sf : Simple Features for R\n\n\n\n\n1.2.1 Format of spatial objects sf\n\n\n\n\n\nObjectssf are objects in data.frame which one of the columns contains geometries. This column is the class of sfc (simple feature column) and each individual of the column is a sfg (simple feature geometry). This format is very practical insofa as the data and the geometries are intrinsically linked in the same object.\n\n\n\n\n\n\nThumbnail describing the simple feature format: Simple Features for R\n\n\n\n\n\n\n\n\n\nTip\n\n\n\nA benchmark of vector processing libraries is available here."
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"text": "1.3 Package mapsf\nThe free R software spatial ecosystem is rich, dynamic and mature and several packages allow to import, process and represent spatial data. The package mapsf (Giraud 2022) relies on this ecosystem to integrate the creation of quality thematic maps into processing chains with R.\nOther packages can be used to make thematic maps. The package ggplot2 (Wickham 2016), in association with the package ggspatial (Dunnington 2021), allows for example to display spatial objects and to make simple thematic maps. The package tmap (Tennekes 2018) is dedicated to the creation of thematic maps, it uses a syntax close to that of ggplot2 (sequence of instructions combined with the ‘+’ sign). Documentation and tutorials for using these two packages are readily available on the web.\nHere, we will mainly use the package mapsf whose functionalities are quite complete and the handling rather simple. In addition, the package is relatively light.\n\nmapsf allows you to create most of the types of map usually used in statistical cartography (choropleth maps, typologies, proportional or graduated symbols, etc.). For each type of map, several parameters are used to customize the cartographic representation. These parameters are the same as those found in the usual GIS or cartography software (for example, the choice of discretizations and color palettes, the modification of the size of the symbols or the customization of the legends). Associated with the data representation functions, other functions are dedicated to cartographic dressing (themes or graphic charters, legends, scales, orientation arrows, title, credits, annotations, etc.), the creation of boxes or the exporting maps.\nmapsf is the successor of cartography (Giraud and Lambert 2016), it offers the same main functionalities while being lighter and more ergonomic.\nTo use this package several sources can be consulted:\n\nThe package documentation accessible on the internet or directly in R (?mapsf),\nA cheat sheet,\n\n\n\n\n\n\n\nThe vignettes associated with the package show sample scripts,\nThe R Geomatics blog which provides resources and examples related to the package and more generally to the R spatial ecosystem."
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"text": "1.4 The package terra\n The package terra was release in early 2020 by Robert J. Hijmans (also author of raster). Its objective is to propose methods of treatment and analysis of raster data. This package is very similar to the package raster; but it has more features, it’s easier to use, and it’s faster.\n\n\n\n\n\n\nWebsite of package terra : Spatial Data Science with R and “terra”\n\n\n\n\n\n\n\n\n\nTip\n\n\n\nA benchmark of raster processing libraries is available here.\n\n\n\n\n\n\nBivand, Roger, Tim Keitt, and Barry Rowlingson. 2022. “Rgdal: Bindings for the ’Geospatial’ Data Abstraction Library.” https://CRAN.R-project.org/package=rgdal.\n\n\nBivand, Roger, and Colin Rundel. 2021. “Rgeos: Interface to Geometry Engine - Open Source (’GEOS’).” https://CRAN.R-project.org/package=rgeos.\n\n\nDunnington, Dewey. 2021. “Ggspatial: Spatial Data Framework for Ggplot2.” https://CRAN.R-project.org/package=ggspatial.\n\n\nGDAL/OGR contributors. n.d. GDAL/OGR Geospatial Data Abstraction Software Library. Open Source Geospatial Foundation. https://gdal.org.\n\n\nGiraud, Timothée. 2022. “Mapsf: Thematic Cartography.” https://CRAN.R-project.org/package=mapsf.\n\n\nGiraud, Timothée, and Nicolas Lambert. 2016. “Cartography: Create and Integrate Maps in Your r Workflow” 1. https://doi.org/10.21105/joss.00054.\n\n\nHijmans, Robert J. 2022a. “Raster: Geographic Data Analysis and Modeling.” https://CRAN.R-project.org/package=raster.\n\n\n———. 2022b. “Terra: Spatial Data Analysis.” https://CRAN.R-project.org/package=terra.\n\n\nPebesma, Edzer. 2018a. “Simple Features for r: Standardized Support for Spatial Vector Data” 10. https://doi.org/10.32614/RJ-2018-009.\n\n\n———. 2018b. “Simple Features for R: Standardized Support for Spatial Vector Data.” The R Journal 10 (1): 439. https://doi.org/10.32614/rj-2018-009.\n\n\n———. 2021. “Stars: Spatiotemporal Arrays, Raster and Vector Data Cubes.” https://CRAN.R-project.org/package=stars.\n\n\nPebesma, Edzer J., and Roger S. Bivand. 2005. “Classes and Methods for Spatial Data in r” 5. https://CRAN.R-project.org/doc/Rnews/.\n\n\nPROJ contributors. 2021. PROJ Coordinate Transformation Software Library. Open Source Geospatial Foundation. https://proj.org/.\n\n\nTennekes, Martijn. 2018. “Tmap: Thematic Maps in r” 84. https://doi.org/10.18637/jss.v084.i06.\n\n\nWickham, Hadley. 2016. “Ggplot2: Elegant Graphics for Data Analysis.” https://ggplot2.tidyverse.org."
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"text": "The function st_as_sf() makes it possible to transform a data.frame container of geographic coordinates into an object sf. Here we use the data.frame places2 created in the previous point.\n\nlibrary(sf)\nplace_sf <- st_as_sf(read.csv(\"data_cambodia/adress.csv\"), coords = c(\"long\", \"lat\"), crs = 4326)\nplace_sf\n\nSimple feature collection with 2 features and 1 field\nGeometry type: POINT\nDimension: XY\nBounding box: xmin: 104.8443 ymin: 11.54366 xmax: 104.9047 ymax: 11.55349\nGeodetic CRS: WGS 84\n address\n1 Phnom Penh International Airport, Phnom Penh, Cambodia\n2 Khmer Soviet Friendship Hospital, Phnom Penh, Cambodia\n geometry\n1 POINT (104.8443 11.55349)\n2 POINT (104.9047 11.54366)\n\n\n\n\nSpherical geometry (s2) switched off\n\n\nTo create a sf POINT type object with only one pair of coordinate (WGS84, longitude=0.5, latitude = 45.5) :\n\nlibrary(sf)\ntest_point <- st_as_sf(data.frame(x = 0.5, y = 45.5), coords = c(\"x\", \"y\"), crs = 4326)\ntest_point\n\nSimple feature collection with 1 feature and 0 fields\nGeometry type: POINT\nDimension: XY\nBounding box: xmin: 0.5 ymin: 45.5 xmax: 0.5 ymax: 45.5\nGeodetic CRS: WGS 84\n geometry\n1 POINT (0.5 45.5)\n\n\nWe can display this object sf on an OpenStreetMap basesmap with the package maptiles maptiles (Giraud 2021).\n\nlibrary(maptiles)\nosm <- get_tiles(x = place_sf, zoom = 12)\nplot_tiles(osm)\nplot(st_geometry(place_sf), pch = 2, cex = 2, col = \"red\", add = TRUE)"
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"text": "2.3 OpenStreetMap\n\n\n\nOpenStreetMap (OSM) is a participatory mapping project that aims to built a free geographic database on a global scale. OpenStreetMap lets you view, edit and use geographic data around the world.\nTerms of use\n\nOpenStreetMap is open data : you are free to use it for ant purpose as long as you credit OpenStreetMap and its contributers. If you modify or rely data in any way, you may distribute the result only under the same license. (…)\n\nContributors\n\n(…) Our contributors incloude enthusiastic mapmakers, GIS professional, engineers running OSM servers, humanitarians mapping disaster-stricken areas and many mmore.(…)\n\n\n2.3.1 Display and interactive map\nThe two main packages that allow to display as interactive map based on OSM are leaflet (Cheng, Karambelkar, and Xie 2022) and mapview (Appelhans et al. 2022).\n\n2.3.1.1 leaflet\n leaflet uses the javascript library Leaflet (Agafonkin 2015) to create interactive maps.\n\nlibrary(sf)\nlibrary(leaflet)\n\ndistrict <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\", quiet = TRUE)\nhospital <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"hospital\", quiet = TRUE)\n\n\nbanan <- district[district$ADM2_PCODE == \"KH0201\", ] #Select one district (Banan district: KH0201)\nhealth_banan <- hospital[hospital$DCODE == \"201\", ] #Select Health centers in Banan\n\nbanan <- st_transform(banan, 4326) #Transform coordinate system to WGS84\nhealth_banan <- st_transform(health_banan, 4326)\n\nbanan_map <- leaflet(banan) %>% #Create interactive map\n addTiles() %>%\n addPolygons() %>%\n addMarkers(data = health_banan)\nbanan_map\n\n\n\n\n\n\n\n\n\n\n\nWebsite of leaflet\nLeaflet for R\n\n\n\n\n\n2.3.1.2 mapview\n mapview relies on leaflet to create interactive maps, its use is easier and its documentation is a bit dense.\n\nlibrary(mapview)\nmapview(banan) + mapview(health_banan)\n\n\n\n\n\n\n\n\n\n\n\n\nWebsite of mapview\nmapview\n\n\n\n\n\n\n2.3.2 Import basemaps\nThe package maptiles (Giraud 2021) allows downlaoding and displaying raster basemaps.\nThe function get_tiles() allow you to download OSM background maps and the function plot_tiles() allows to display them.\nRenders are better if the input data used the same coordinate system as the tiles (EPSG:3857).\n\nlibrary(sf)\nlibrary(maptiles)\ndistrict <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\", quiet = TRUE)\ndistrict <- st_transform(district, 3857)\nosm_tiles <- get_tiles(x = district, zoom = 10, crop = TRUE)\nplot_tiles(osm_tiles)\nplot(st_geometry(district), border = \"grey20\", lwd = .7, add = TRUE)\nmtext(side = 1, line = -2, text = get_credit(\"OpenStreetMap\"), col=\"tomato\")\n\n\n\n\n\n\n2.3.3 Import OSM data\n\n2.3.3.1 osmdata\n The package osmdata (Padgham et al. 2017) allows extracting vector data from OSM using the Overpass turbo API.\n\nlibrary(sf)\nlibrary(osmdata)\nlibrary(sf)\n\ncountry <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"country\", quiet = TRUE)\next <- opq(bbox = st_bbox(st_transform(country, 4326))) #Define the bounding box\nquery <- add_osm_feature(opq = ext, key = 'amenity', value = \"hospital\") #Health Center Extraction\nhospital <- osmdata_sf(query)\nhospital <- unique_osmdata(hospital) #Result reduction (points composing polygon are detected)\n\nThe result contains a point layer and a polygon layer. The polygon layer contains polygons that represent fast food-food place. To obtain a coherent point layer we can use the centroids of the polygons.\n\nhospital_point <- hospital$osm_points\nhospital_poly <- hospital$osm_polygons #Extracting centroids of polygons\nhospital_poly_centroid <- st_centroid(hospital_poly)\n\ncambodia_point <- intersect(names(hospital_point), names(hospital_poly_centroid)) #Identify fields in Cambodia boundary\nhospitals <- rbind(hospital_point[, cambodia_point], hospital_poly_centroid[, cambodia_point]) #Gather the 2 objects\n\nResult display\n\nlibrary(mapview)\nmapview(country) + mapview(hospitals)\n\n\n\n\n\n\n\n\n\n\n\n\nWebsite of osmdata\nosmdata\n\n\n\n\n\n2.3.3.2 osmextract\n The package osmextract (Gilardi and Lovelace 2021) allows to extract data from an OSM database directly. This package make it possible to work on very large volumes of data.\n\n\n\n\n\n\nWebsite of osmextract\nosmextract\n\n\n\nFor administrative boundaries, check here the administrative levels by country:\n\nlibrary(osmextract)\nlibrary(mapsf)\nprovince <- oe_get(\n place = \"Cambodia\",\n download_directory = \"data_cambodia/\",\n layer = \"multipolygons\",\n extra_tags = c(\"wikidata\", \"ISO3166-2\", \"wikipedia\", \"name:en\"),\n vectortranslate_options = c(\n \"-t_srs\", \"EPSG:32648\",\n \"-nlt\", \"PROMOTE_TO_MULTI\",\n \"-where\", \"type = 'boundary' AND boundary = 'administrative' AND admin_level = '4'\"\n ))\n\n0...10...20...30...40...50...60...70...80...90...100 - done.\nReading layer `multipolygons' from data source \n `/home/lucas/Documents/Framagit/rspatial-for-onehealth/data_cambodia/geofabrik_cambodia-latest.gpkg' \n using driver `GPKG'\nSimple feature collection with 25 features and 29 fields\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: 211418.1 ymin: 1047956 xmax: 784614.9 ymax: 1625621\nProjected CRS: WGS 84 / UTM zone 48N\n\nmf_map(x = province)\n\n\n\n\n\nroads <- oe_get(\n place = \"Cambodia\",\n download_directory = \"data_cambodia/\",\n layer = \"lines\",\n extra_tags = c(\"access\", \"service\", \"maxspeed\"),\n vectortranslate_options = c(\n \"-t_srs\", \"EPSG:32648\",\n \"-nlt\", \"PROMOTE_TO_MULTI\",\n \"-where\", \"\n highway IS NOT NULL\n AND\n highway NOT IN (\n 'abandonded', 'bus_guideway', 'byway', 'construction', 'corridor', 'elevator',\n 'fixme', 'escalator', 'gallop', 'historic', 'no', 'planned', 'platform',\n 'proposed', 'cycleway', 'pedestrian', 'bridleway', 'footway',\n 'steps', 'path', 'raceway', 'road', 'service', 'track'\n )\n \"\n),\n boundary = subset(province, name_en == \"Phnom Penh\"),\n boundary_type = \"clipsrc\"\n)\n\n0...10...20...30...40...50...60...70...80...90...100 - done.\nReading layer `lines' from data source \n `/home/lucas/Documents/Framagit/rspatial-for-onehealth/data_cambodia/geofabrik_cambodia-latest.gpkg' \n using driver `GPKG'\nSimple feature collection with 18794 features and 12 fields\nGeometry type: MULTILINESTRING\nDimension: XY\nBounding box: xmin: 469524.2 ymin: 1263268 xmax: 503494.3 ymax: 1296780\nProjected CRS: WGS 84 / UTM zone 48N\n\nmf_map(x = roads)"
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"text": "2.4 Geocoding\nServeral pakages alow you to geocode addresses. The package tidygeocoder (Cambon et al. 2021) allow the use of a large number of online geocoding sevices. The package banR (Gombin and Chevalier 2022), which is based on the National Address Base, is the particularly suitable for geocoding addresses in France.\n\n2.4.1 tidygeocoder\n\nlibrary(tidygeocoder)\ntest_adresses <- data.frame(\n address = c(\"Phnom Penh International Airport, Phnom Penh, Cambodia\",\n \"Khmer Soviet Friendship Hospital, Phnom Penh, Cambodia\"))\nplaces1 <- geocode(test_adresses, address)\nplaces1\n\n# A tibble: 2 × 3\n address lat long\n <chr> <dbl> <dbl>\n1 Phnom Penh International Airport, Phnom Penh, Cambodia 11.6 105.\n2 Khmer Soviet Friendship Hospital, Phnom Penh, Cambodia 11.5 105.\n\n\n\n\n\n\n\n\nWebsite by tidygeocoder :\ntidygeocoder\n\n\n\n\n\n2.4.2 banR (Base Adresse Nationale)\n\n# remotes::install_github(\"joelgombin/banR\")\nlibrary(banR)\nmes_adresses <- data.frame(\n address = c(\"19 rue Michel Bakounine, 29600 Morlaix, France\",\n \"2 Allee Emile Pouget, 920128 Boulogne-Billancourt\")\n)\nplaces2 <- geocode_tbl(tbl = mes_adresses, adresse = address)\nplaces2\n\n# A tibble: 2 × 18\n address latit…¹ longi…² resul…³ resul…⁴ resul…⁵ resul…⁶ resul…⁷ resul…⁸\n <chr> <dbl> <dbl> <chr> <dbl> <chr> <chr> <chr> <chr> \n1 19 rue Michel… 48.6 -3.82 19 Rue… 0.81 housen… 29151_… 19 Rue Mi…\n2 2 Allee Emile… 48.8 2.24 2 Allé… 0.83 housen… 92012_… 2 Allée …\n# … with 9 more variables: result_street <chr>, result_postcode <chr>,\n# result_city <chr>, result_context <chr>, result_citycode <chr>,\n# result_oldcitycode <chr>, result_oldcity <chr>, result_district <chr>,\n# result_status <chr>, and abbreviated variable names ¹latitude, ²longitude,\n# ³result_label, ⁴result_score, ⁵result_type, ⁶result_id,\n# ⁷result_housenumber, ⁸result_name\n\n\n\n\n\n\n\n\nWebsite of banR :\nAn R client for the BAN API"
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"text": "2.5 Digitization\nThe package mapedit (Appelhans, Russell, and Busetto 2020) allows you to digitize base map directly in R. Although it can be practical in some cases, in package cannot replace the functionalities of a GIS for important digitization tasks.\n\n\n\nGif taken from mapedit website\n\n\n\n\n\n\nAgafonkin, Vladimir. 2015. “Leaflet Javascript Libary.”\n\n\nAppelhans, Tim, Florian Detsch, Christoph Reudenbach, and Stefan Woellauer. 2022. “Mapview: Interactive Viewing of Spatial Data in r.” https://CRAN.R-project.org/package=mapview.\n\n\nAppelhans, Tim, Kenton Russell, and Lorenzo Busetto. 2020. “Mapedit: Interactive Editing of Spatial Data in r.” https://CRAN.R-project.org/package=mapedit.\n\n\nCambon, Jesse, Diego Hernangómez, Christopher Belanger, and Daniel Possenriede. 2021. “Tidygeocoder: An r Package for Geocoding” 6: 3544. https://doi.org/10.21105/joss.03544.\n\n\nCheng, Joe, Bhaskar Karambelkar, and Yihui Xie. 2022. “Leaflet: Create Interactive Web Maps with the JavaScript ’Leaflet’ Library.” https://CRAN.R-project.org/package=leaflet.\n\n\nGilardi, Andrea, and Robin Lovelace. 2021. “Osmextract: Download and Import Open Street Map Data Extracts.” https://CRAN.R-project.org/package=osmextract.\n\n\nGiraud, Timothée. 2021. “Maptiles: Download and Display Map Tiles.” https://CRAN.R-project.org/package=maptiles.\n\n\nGombin, Joel, and Paul-Antoine Chevalier. 2022. “banR: R Client for the BAN API.”\n\n\nPadgham, Mark, Bob Rudis, Robin Lovelace, and Maëlle Salmon. 2017. “Osmdata” 2. https://doi.org/10.21105/joss.00305." },
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"text": "The st_read() and st_write() function are used to import and export many types of files. The following lines import the administrative data in district level layer located in the cambodia.gpkg geopackage file.\n\nlibrary(sf)\n\ndistrict = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\") #import district data\n\nReading layer `district' from data source \n `/home/lucas/Documents/Framagit/rspatial-for-onehealth/data_cambodia/cambodia.gpkg' \n using driver `GPKG'\nSimple feature collection with 197 features and 10 fields\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: 211534.7 ymin: 1149105 xmax: 784612.1 ymax: 1625495\nProjected CRS: WGS 84 / UTM zone 48N\n\n\nThe following lines export the district object to a data folder in geopackage and shapefile format.\n\nst_write(obj = district, dsn = \"data_cambodia/district.gpkg\", delete_layer = TRUE)\n\nDeleting layer `district' using driver `GPKG'\nWriting layer `district' to data source \n `data_cambodia/district.gpkg' using driver `GPKG'\nWriting 197 features with 10 fields and geometry type Multi Polygon.\n\nst_write(obj = district, \"data_cambodia/district.shp\", layer_options = \"ENCODING=UTF-8\", delete_layer = TRUE)\n\nDeleting layer `district' using driver `ESRI Shapefile'\nWriting layer `district' to data source \n `data_cambodia/district.shp' using driver `ESRI Shapefile'\noptions: ENCODING=UTF-8 \nWriting 197 features with 10 fields and geometry type Multi Polygon."
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"text": "3.2 Display\nPreview of the variables via the function head() and plot().\n\nhead(district)\n\nSimple feature collection with 6 features and 10 fields\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: 300266.9 ymin: 1180566 xmax: 767313.9 ymax: 1563861\nProjected CRS: WGS 84 / UTM zone 48N\n ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE Male Female T_POP Area.Km2.\n1 Aek Phnum KH0205 Battambang KH02 41500 43916 85416 1067.8638\n2 Andoung Meas KH1601 Ratanak Kiri KH16 7336 7372 14708 837.7064\n3 Angk Snuol KH0808 Kandal KH08 45436 47141 92577 183.9050\n4 Angkor Borei KH2101 Takeo KH21 26306 27168 53474 301.0502\n5 Angkor Chey KH0701 Kampot KH07 42448 44865 87313 316.7576\n6 Angkor Chum KH1701 Siemreap KH17 34269 34576 68845 478.6988\n Status DENs geom\n1 <4500km2 79.98773 MULTIPOLYGON (((306568.1 14...\n2 <4500km2 17.55747 MULTIPOLYGON (((751459.2 15...\n3 <4500km2 503.39580 MULTIPOLYGON (((471954.3 12...\n4 <4500km2 177.62485 MULTIPOLYGON (((490048.2 12...\n5 <4500km2 275.64610 MULTIPOLYGON (((462702.2 12...\n6 <4500km2 143.81696 MULTIPOLYGON (((363642.5 15...\n\nplot(district)\n\n\n\n\nfor Geometry display only.\n\nplot(st_geometry(district))"
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"text": "3.3 Coordinate systems\n\n3.3.1 Look up the coordinate system of an object\nThe function st_crs() makes it possible to consult the system of coordinates used and object sf.\n\nst_crs(district)\n\nCoordinate Reference System:\n User input: WGS 84 / UTM zone 48N \n wkt:\nPROJCRS[\"WGS 84 / UTM zone 48N\",\n BASEGEOGCRS[\"WGS 84\",\n ENSEMBLE[\"World Geodetic System 1984 ensemble\",\n MEMBER[\"World Geodetic System 1984 (Transit)\"],\n MEMBER[\"World Geodetic System 1984 (G730)\"],\n MEMBER[\"World Geodetic System 1984 (G873)\"],\n MEMBER[\"World Geodetic System 1984 (G1150)\"],\n MEMBER[\"World Geodetic System 1984 (G1674)\"],\n MEMBER[\"World Geodetic System 1984 (G1762)\"],\n MEMBER[\"World Geodetic System 1984 (G2139)\"],\n ELLIPSOID[\"WGS 84\",6378137,298.257223563,\n LENGTHUNIT[\"metre\",1]],\n ENSEMBLEACCURACY[2.0]],\n PRIMEM[\"Greenwich\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433]],\n ID[\"EPSG\",4326]],\n CONVERSION[\"UTM zone 48N\",\n METHOD[\"Transverse Mercator\",\n ID[\"EPSG\",9807]],\n PARAMETER[\"Latitude of natural origin\",0,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8801]],\n PARAMETER[\"Longitude of natural origin\",105,\n ANGLEUNIT[\"degree\",0.0174532925199433],\n ID[\"EPSG\",8802]],\n PARAMETER[\"Scale factor at natural origin\",0.9996,\n SCALEUNIT[\"unity\",1],\n ID[\"EPSG\",8805]],\n PARAMETER[\"False easting\",500000,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8806]],\n PARAMETER[\"False northing\",0,\n LENGTHUNIT[\"metre\",1],\n ID[\"EPSG\",8807]]],\n CS[Cartesian,2],\n AXIS[\"(E)\",east,\n ORDER[1],\n LENGTHUNIT[\"metre\",1]],\n AXIS[\"(N)\",north,\n ORDER[2],\n LENGTHUNIT[\"metre\",1]],\n USAGE[\n SCOPE[\"Engineering survey, topographic mapping.\"],\n AREA[\"Between 102°E and 108°E, northern hemisphere between equator and 84°N, onshore and offshore. Cambodia. China. Indonesia. Laos. Malaysia - West Malaysia. Mongolia. Russian Federation. Singapore. Thailand. Vietnam.\"],\n BBOX[0,102,84,108]],\n ID[\"EPSG\",32648]]\n\n\n\n\n3.3.2 Changing the coordinate system of an object\nThe function st_transform() allows to change the coordinate system of an sf object, to re-project it.\n\nplot(st_geometry(district))\ntitle(\"WGS 84 / UTM zone 48N\")\n\n\n\ndist_reproj <- st_transform(district, \"epsg:4326\")\nplot(st_geometry(dist_reproj))\ntitle(\"WGS84\")\n\n\n\n\nThe Spatial Reference site provides reference for a large number of coordinate systems."
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"text": "3.4 Selection by attributes\nThe object sf are data.frame, so you can select their rows and columns in the same way as data.frame.\n\n# row Selection\ndistrict[1:2, ]\n\nSimple feature collection with 2 features and 10 fields\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: 300266.9 ymin: 1449408 xmax: 767313.9 ymax: 1563861\nProjected CRS: WGS 84 / UTM zone 48N\n ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE Male Female T_POP Area.Km2.\n1 Aek Phnum KH0205 Battambang KH02 41500 43916 85416 1067.8638\n2 Andoung Meas KH1601 Ratanak Kiri KH16 7336 7372 14708 837.7064\n Status DENs geom\n1 <4500km2 79.98773 MULTIPOLYGON (((306568.1 14...\n2 <4500km2 17.55747 MULTIPOLYGON (((751459.2 15...\n\ndistrict[district$ADM1_EN == \"Phnom Penh\", ]\n\nSimple feature collection with 12 features and 10 fields\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: 468677.5 ymin: 1262590 xmax: 505351.9 ymax: 1297419\nProjected CRS: WGS 84 / UTM zone 48N\nFirst 10 features:\n ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE Male Female T_POP\n29 Chamkar Mon KH1201 Phnom Penh KH12 52278 54478 106756\n31 Chbar Ampov KH1212 Phnom Penh KH12 64816 68243 133059\n43 Chraoy Chongvar KH1210 Phnom Penh KH12 30920 31087 62007\n48 Dangkao KH1205 Phnom Penh KH12 46999 48525 95524\n50 Doun Penh KH1202 Phnom Penh KH12 33844 36471 70315\n93 Mean Chey KH1206 Phnom Penh KH12 68381 70366 138747\n117 Praek Pnov KH1211 Phnom Penh KH12 27566 27698 55264\n118 Prampir Meakkakra KH1203 Phnom Penh KH12 31091 33687 64778\n133 Pur SenChey KH1209 Phnom Penh KH12 95050 109297 204347\n141 Russey Keo KH1207 Phnom Penh KH12 67357 68419 135776\n Area.Km2. Status DENs geom\n29 11.049600 <4500km2 9661.5265 MULTIPOLYGON (((494709.4 12...\n31 86.780498 <4500km2 1533.2823 MULTIPOLYGON (((498855.3 12...\n43 85.609156 <4500km2 724.3034 MULTIPOLYGON (((491161.3 12...\n48 113.774833 <4500km2 839.5881 MULTIPOLYGON (((489191.1 12...\n50 7.734808 <4500km2 9090.7234 MULTIPOLYGON (((492447.1 12...\n93 28.998026 <4500km2 4784.7051 MULTIPOLYGON (((491068.2 12...\n117 115.384300 <4500km2 478.9560 MULTIPOLYGON (((481483.3 12...\n118 2.224892 <4500km2 29115.1253 MULTIPOLYGON (((491067.6 12...\n133 148.357984 <4500km2 1377.3913 MULTIPOLYGON (((479078.8 12...\n141 23.381517 <4500km2 5806.9800 MULTIPOLYGON (((490264.8 12...\n\n# column selection\ndistrict[district$ADM1_EN == \"Phnom Penh\", 1:4] \n\nSimple feature collection with 12 features and 4 fields\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: 468677.5 ymin: 1262590 xmax: 505351.9 ymax: 1297419\nProjected CRS: WGS 84 / UTM zone 48N\nFirst 10 features:\n ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE\n29 Chamkar Mon KH1201 Phnom Penh KH12\n31 Chbar Ampov KH1212 Phnom Penh KH12\n43 Chraoy Chongvar KH1210 Phnom Penh KH12\n48 Dangkao KH1205 Phnom Penh KH12\n50 Doun Penh KH1202 Phnom Penh KH12\n93 Mean Chey KH1206 Phnom Penh KH12\n117 Praek Pnov KH1211 Phnom Penh KH12\n118 Prampir Meakkakra KH1203 Phnom Penh KH12\n133 Pur SenChey KH1209 Phnom Penh KH12\n141 Russey Keo KH1207 Phnom Penh KH12\n geom\n29 MULTIPOLYGON (((494709.4 12...\n31 MULTIPOLYGON (((498855.3 12...\n43 MULTIPOLYGON (((491161.3 12...\n48 MULTIPOLYGON (((489191.1 12...\n50 MULTIPOLYGON (((492447.1 12...\n93 MULTIPOLYGON (((491068.2 12...\n117 MULTIPOLYGON (((481483.3 12...\n118 MULTIPOLYGON (((491067.6 12...\n133 MULTIPOLYGON (((479078.8 12...\n141 MULTIPOLYGON (((490264.8 12..."
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"text": "3.5 Spatial selection\n\n3.5.1 Intersections\nSelection of roads that are intersecting dangkao district\n\nroad <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"road\", quiet = TRUE) %>% st_cast(\"LINESTRING\")\ndangkao <- district[district$ADM2_EN == \"Dangkao\", ]\ninter <- st_intersects(x = road, y = dangkao, sparse = FALSE)\nhead(inter)\n\n [,1]\n[1,] FALSE\n[2,] FALSE\n[3,] FALSE\n[4,] FALSE\n[5,] FALSE\n[6,] FALSE\n\ndim(inter)\n\n[1] 108285 1\n\n\nThe inter object is a matrix which indicates for each of element of the road object (6 elements) whether it intersects each elements the dangkao object (1 element). The dimension of the matrix is therefore indeed 6 rows * 1 column. Note the use of the parameter sparse = FALSE here. It is then possible to create a column from this object:\n\nroad$intersect_dangkao <- inter\nplot(st_geometry(dangkao), col = \"lightblue\")\nplot(st_geometry(road), add = TRUE)\nplot(st_geometry(road[road$intersect_dangkao, ]),\n col = \"tomato\", lwd = 1.5, add = TRUE)\n\n\n\n\n\n3.5.1.1 Difference between sparse = TRUE and sparse = FALSE\n\n\n\n\n\n\nsparse = TRUE\n\n\ninter <- st_intersects(x = grid, y = pt, sparse = TRUE)\ninter\n\nSparse geometry binary predicate list of length 4, where the predicate\nwas `intersects'\n 1: (empty)\n 2: 6, 7\n 3: 1, 4\n 4: 2, 3, 5, 8\n\n\n\nsparse = FALSE\n\n\ninter <- st_intersects(x = grid, y = pt, sparse = FALSE)\nrownames(inter) <- grid$id\ncolnames(inter) <- pt$id\ninter\n\n a b c d e f g h\n1 FALSE FALSE FALSE FALSE FALSE FALSE FALSE FALSE\n2 FALSE FALSE FALSE FALSE FALSE TRUE TRUE FALSE\n3 TRUE FALSE FALSE TRUE FALSE FALSE FALSE FALSE\n4 FALSE TRUE TRUE FALSE TRUE FALSE FALSE TRUE\n\n\n\n\n\n3.5.2 Contains / Within\nSelection of roads contained in the municipality of Dangkao. The function st_within() works like the function st_intersects()\n\nroad$within_dangkao <- st_within(road, dangkao, sparse = FALSE)\nplot(st_geometry(dangkao), col = \"lightblue\")\nplot(st_geometry(road), add = TRUE)\nplot(st_geometry(road[road$within_dangkao, ]), col = \"tomato\",\n lwd = 2, add = TRUE)"
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"text": "3.6 Operation of geometries\n\n3.6.1 Extract centroids\n\ndist_c <- st_centroid(district)\nplot(st_geometry(district))\nplot(st_geometry(dist_c), add = TRUE, cex = 1.2, col = \"red\", pch = 20)\n\n\n\n\n\n\n3.6.2 Aggregate polygons\n\ncambodia_dist <- st_union(district) \nplot(st_geometry(district), col = \"lightblue\")\nplot(st_geometry(cambodia_dist), add = TRUE, lwd = 2, border = \"red\")\n\n\n\n\n\n\n3.6.3 Aggregate polygons based on a variable\n\ndist_union <- aggregate(x = district[,c(\"T_POP\")],\n by = list(STATUT = district$Status),\n FUN = \"sum\")\nplot(dist_union)\n\n\n\n\n\n\n3.6.4 Create a buffer zone\n\ndangkao_buffer <- st_buffer(x = dangkao, dist = 1000)\nplot(st_geometry(dangkao_buffer), col = \"#E8DAEF\", lwd=2, border = \"#6C3483\")\nplot(st_geometry(dangkao), add = TRUE, lwd = 2)\n\n\n\n\n\n\n3.6.5 Making an intersection\nBy using the function st_intersection() we will cut one layer by another.\n\nlibrary(magrittr)\n# creation of a buffer zone around the centroid of the municipality of Dangkao district\n# using the pipe\nzone <- st_geometry(dangkao) %>%\n st_centroid() %>%\n st_buffer(30000)\nplot(st_geometry(district))\nplot(zone, border = \"#F06292\", lwd = 2, add = TRUE)\n\n\n\ndist_z <- st_intersection(x = district, y = zone)\nplot(st_geometry(district))\nplot(st_geometry(dist_z), col=\"#AF7AC5\", border=\"#F9E79F\", add=T)\n\n\n\nplot(st_geometry(dist_z))\n\n\n\n\n\n\n3.6.6 Create regular grid\nThe function st_make_grid() allows you to create regular grid. The function produce and object sfc, you must then use the function st_sf() to transform the object sfc into and object sf. During this transformation we add here a column of unique identifiers.\n\ngrid <- st_make_grid(x = district, cellsize = 10000)\ngrid <- st_sf(ID = 1:length(grid), geom = grid)\n\nplot(st_geometry(grid), col = \"grey\", border = \"white\")\nplot(st_geometry(district), border = \"grey50\", add = TRUE)\n\n\n\n\n\n\n3.6.7 Counting points in a polygon (in a grid tile)\n\n# selection of grid tiles that intersect the district\n\ninter <- st_intersects(grid, cambodia_dist, sparse = FALSE)\ngrid <- grid[inter, ]\n\ncase_cambodia <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"cases\" , quiet = TRUE)\nplot(st_geometry(grid), col = \"grey\", border = \"white\")\nplot(st_geometry(case_cambodia), pch = 20, col = \"red\", add = TRUE, cex = 0.8)\n\n\n\ninter <- st_intersects(grid, case_cambodia, sparse = TRUE)\nlength(inter)\n\n[1] 1964\n\n\nHere we use the argument sparse = TRUE. The inter object is a list the length of the grid and each item in the list contain the index of the object items of cases and grid intersection.\nFor example grid tile 35th intersect with four cases 97, 138, 189, 522, 624, 696\n\ninter[35]\n\n[[1]]\n[1] 97 138 189 522 624 696\n\nplot(st_geometry(grid[35, ]))\nplot(st_geometry(case_cambodia), add = T)\nplot(st_geometry(case_cambodia[c(97, 138, 189, 522, 624, 696), ]), \n col = \"red\", pch = 19, add = TRUE)\n\n\n\n\nTo count number of case, simply go to the list and report length of the elements.\n\ngrid$nb_case <- sapply(X = inter, FUN = length) # create 'nb_case' column to store number of health centers in each grid tile \nplot(grid[\"nb_case\"])\n\n\n\n\n\n\n3.6.8 Aggregate point values into polygons\nIn this example we import a csv file that contain data from a population grid. Once import we transform it data.frame into an object sf.\nThe objective is to aggregate the values id these points (the population contained in the “DENs” field) in the municipalities of the district.\n\npp_pop_raw <- read.csv(\"data_cambodia/pp_pop_dens.csv\") # import file\npp_pop_raw$id <- 1:nrow(pp_pop_raw) # adding a unique identifier\npp_pop <- st_as_sf(pp_pop_raw, coords = c(\"X\", \"Y\"), crs = 32648) # Transform into object sf\npp_pop <- st_transform(pp_pop, st_crs(district)) # Transform projection\ninter <- st_intersection(pp_pop, district) # Intersection\ninter\n\nSimple feature collection with 1295 features and 12 fields\nGeometry type: POINT\nDimension: XY\nBounding box: xmin: 469177.5 ymin: 1263090 xmax: 505177.5 ymax: 1297090\nProjected CRS: WGS 84 / UTM zone 48N\nFirst 10 features:\n DENs id ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE Male Female T_POP\n149 NA 149 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n150 NA 150 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n151 NA 151 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n186 NA 186 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n187 NA 187 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n188 NA 188 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n223 NA 223 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n224 NA 224 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n225 NA 225 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n226 3.400075 226 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n Area.Km2. Status DENs.1 geometry\n149 183.905 <4500km2 503.3958 POINT (469177.5 1267090)\n150 183.905 <4500km2 503.3958 POINT (470177.5 1267090)\n151 183.905 <4500km2 503.3958 POINT (471177.5 1267090)\n186 183.905 <4500km2 503.3958 POINT (469177.5 1268090)\n187 183.905 <4500km2 503.3958 POINT (470177.5 1268090)\n188 183.905 <4500km2 503.3958 POINT (471177.5 1268090)\n223 183.905 <4500km2 503.3958 POINT (469177.5 1269090)\n224 183.905 <4500km2 503.3958 POINT (470177.5 1269090)\n225 183.905 <4500km2 503.3958 POINT (471177.5 1269090)\n226 183.905 <4500km2 503.3958 POINT (472177.5 1269090)\n\n\nBy using the function st_intersection() we add to each point of the grid all the information on the municipality in which it is located.\nWe can then use the function aggregate() to aggregate the population by municipalities.\n\nresultat <- aggregate(x = list(pop_from_grid = inter$DENs), \n by = list(ADM2_EN = inter$ADM2_EN), \n FUN = \"sum\")\nhead(resultat)\n\n ADM2_EN pop_from_grid\n1 Angk Snuol NA\n2 Chamkar Mon 10492.7159\n3 Chbar Ampov 1593.9593\n4 Chraoy Chongvar 1434.1785\n5 Dangkao 942.3595\n6 Doun Penh 10781.8026\n\n\nWe can then create a new object with this result.\n\ndist_result <- merge(district, resultat, by = \"ADM2_EN\", all.x = TRUE)\ndist_result\n\nSimple feature collection with 197 features and 11 fields\nGeometry type: MULTIPOLYGON\nDimension: XY\nBounding box: xmin: 211534.7 ymin: 1149105 xmax: 784612.1 ymax: 1625495\nProjected CRS: WGS 84 / UTM zone 48N\nFirst 10 features:\n ADM2_EN ADM2_PCODE ADM1_EN ADM1_PCODE Male Female T_POP\n1 Aek Phnum KH0205 Battambang KH02 41500 43916 85416\n2 Andoung Meas KH1601 Ratanak Kiri KH16 7336 7372 14708\n3 Angk Snuol KH0808 Kandal KH08 45436 47141 92577\n4 Angkor Borei KH2101 Takeo KH21 26306 27168 53474\n5 Angkor Chey KH0701 Kampot KH07 42448 44865 87313\n6 Angkor Chum KH1701 Siemreap KH17 34269 34576 68845\n7 Angkor Thum KH1702 Siemreap KH17 13802 14392 28194\n8 Anlong Veaeng KH2201 Oddar Meanchey KH22 24122 23288 47410\n9 Aoral KH0504 Kampong Speu KH05 19874 19956 39830\n10 Ba Phnum KH1401 Prey Veng KH14 46562 49852 96414\n Area.Km2. Status DENs pop_from_grid geometry\n1 1067.8638 <4500km2 79.98773 NA MULTIPOLYGON (((306568.1 14...\n2 837.7064 <4500km2 17.55747 NA MULTIPOLYGON (((751459.2 15...\n3 183.9050 <4500km2 503.39580 NA MULTIPOLYGON (((471954.3 12...\n4 301.0502 <4500km2 177.62485 NA MULTIPOLYGON (((490048.2 12...\n5 316.7576 <4500km2 275.64610 NA MULTIPOLYGON (((462702.2 12...\n6 478.6988 <4500km2 143.81696 NA MULTIPOLYGON (((363642.5 15...\n7 357.8890 <4500km2 78.77862 NA MULTIPOLYGON (((376584.4 15...\n8 1533.5702 <4500km2 30.91479 NA MULTIPOLYGON (((404936.4 15...\n9 2381.7084 <4500km2 16.72329 NA MULTIPOLYGON (((414000.6 13...\n10 342.3439 <4500km2 281.62910 NA MULTIPOLYGON (((545045.4 12..."
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"text": "3.7 Measurements\n\n3.7.1 Create a distance matrix\nIf the dataset’s projection system is specified, the distance are expressed in the projection measurement unit (most often in meter)\n\nmat <- st_distance(x = dist_c, y = dist_c)\nmat[1:5,1:5]\n\nUnits: [m]\n [,1] [,2] [,3] [,4] [,5]\n[1,] 0.0 425993.7 232592.12 298254.12 299106.92\n[2,] 425993.7 0.0 386367.88 414428.82 452431.87\n[3,] 232592.1 386367.9 0.00 67060.05 82853.88\n[4,] 298254.1 414428.8 67060.05 0.00 40553.15\n[5,] 299106.9 452431.9 82853.88 40553.15 0.00\n\n\n\n\n3.7.2 Calculate routes\n The package osrm (R-osrm?) acts as an interface R and the OSRM (luxen-vetter-2011?). This package allows to calculate time and distance matrices, road routes, isochrones. The package uses the OSRM demo server by default. In case of intensive use it is strongly recommended to use your own instance of OSRM (with Docker).\n\n3.7.2.1 Calculate a route\nThe fonction osrmRoute() allows you to calculate routes.\n\nlibrary(sf)\nlibrary(osrm)\nlibrary(maptiles)\ndistrict <- st_read(\"data_cambodia/cambodia.gpkg\",layer = \"district\", quiet = TRUE)\ndistrict <- st_transform(district, 32648)\n\nodongk <- district[district$ADM2_PCODE == \"KH0505\", ] # Itinerary between Odongk district and Toul Kouk\ntakmau <- district[district$ADM2_PCODE == \"KH0811\",]\nroute <- osrmRoute(src = odongk, \n dst = takmau, \n returnclass = \"sf\")\nosm <- get_tiles(route, crop = TRUE)\nplot_tiles(osm)\nplot(st_geometry(route), col = \"#b23a5f\", lwd = 6, add = T)\nplot(st_geometry(route), col = \"#eee0e5\", lwd = 1, add = T)\n\n\n\n\n\n\n3.7.2.2 Calculation of a time matrix\nThe function osrmTable() makes it possible to calculate matrices of distances or times by road.\nIn this example we calculate a time matrix between 2 addresses and health centers in Phnom Penh on foot.\n\nlibrary(sf)\nlibrary(tidygeocoder)\nhospital <- st_read(\"data_cambodia/cambodia.gpkg\",layer= \"hospital\", quiet = TRUE)\n\nhospital_pp <- hospital[hospital$PCODE == \"12\", ] # Selection of health centers in Phnom Penh\n\nadresses <- data.frame(adr = c(\"Royal Palace Park, Phnom Penh Phnom, Cambodia\",\n \"Wat Phnom Daun Penh, Phnom Penh, Cambodia\")) # Geocoding of 2 addresses in Phnom Penh\n\nplaces <- tidygeocoder::geocode(.tbl = adresses,address = adr)\nplaces\n\n# A tibble: 2 × 3\n adr lat long\n <chr> <dbl> <dbl>\n1 Royal Palace Park, Phnom Penh Phnom, Cambodia 11.6 105.\n2 Wat Phnom Daun Penh, Phnom Penh, Cambodia 11.6 105.\n\n# Calculation of the distance matrix between the 2 addresses and the health center in Phnom Penh\n\ncal_mat <- osrmTable(src = places[,c(1,3,2)], \n dst = hospital_pp, \n osrm.profile = \"foot\")\n\ncal_mat$durations[1:2, 1:5]\n\n 684 685 686 687 691\nRoyal Palace Park, Phnom Penh Phnom, Cambodia 55.9 71.6 64.4 40.2 76.7\nWat Phnom Daun Penh, Phnom Penh, Cambodia 60.1 80.4 40.1 32.8 53.1\n\n# Which address has better accessibility to health center in Phnom Penh?\n\nboxplot(t(cal_mat$durations[,]), cex.axis = 0.7)"
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"text": "This chapter is largely inspired by two presentation; Madelin (2021) and Nowosad (2021); carried out as part of the SIGR2021 thematic school."
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"text": "4.1 Format of objects SpatRaster\nThe package terra (Hijmans 2022) allows to handle vector and raster data. To manipulate this spatial data, terra store it in object of type SpatVector and SpatRaster. In this chapter, we focus on the manipulation of raster data (SpatRaster) from functions offered by this package.\nAn object SpatRaster allows to handle vector and raster data, in one or more layers (variables). This object also stores a number of fundamental parameters that describe it (number of columns, rows, spatial extent, coordinate reference system, etc.).\n\n\n\nSource : (Racine 2016)"
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"text": "4.2 Importing and exporting data\nThe package terra allows importing and exporting raster files. It is based on the GDAL library which makes it possible to read and process a very large number of geographic image formats.\n\nlibrary(terra)\n\nThe function rast() allows you to create and/or import raster data. The following lines import the raster file elevation.tif (Tagged Image File Format) into an object of type SpatRaster (default).\n\nelevation <- rast(\"data_cambodia/elevation.tif\") \nelevation\n\nclass : SpatRaster \ndimensions : 5235, 6458, 1 (nrow, ncol, nlyr)\nresolution : 0.0008333394, 0.0008332568 (x, y)\nextent : 102.2935, 107.6752, 10.33984, 14.70194 (xmin, xmax, ymin, ymax)\ncoord. ref. : lon/lat WGS 84 (EPSG:4326) \nsource : elevation.tif \nname : elevation \n\n\nModifying the name of the stored variable (altitude).\n\nnames(elevation) <- \"Altitude\" \n\nThe function writeRaster() allow you to save an object SpatRaster on your machine, in the format of your choice.\n\nwriteRaster(x = elevation, filename = \"data_cambodia/new_elevation.tif\")" },
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"text": "4.3 Displaying a SpatRaster object\nThe function plot() is use to display an object SpatRaster.\n\nplot(elevation)\n\n\n\n\n\n\n\n\nA raster always contains numerical data, but it can be both quantitative data and numerically coded qualitative (categorical) data (ex: type of land cover).\nSpecify the type of data stored with the augment type (type = \"continuous\" default), to display them correctly.\nImport and display of raster containing categorical data: Phnom Penh Land Cover 2019 (land cover types) with a resolution of 1.5 meters:\n\nlulc_2019 <- rast(\"data_cambodia/lulc_2019.tif\") #Import Phnom Penh landcover 2019, landcover types\n\nThe landcover data was produced from SPOT7 satellite image with 1.5 meter spatial resolution. An extraction centered on the municipality of Phnom Penh was then carried out.\n\nplot(lulc_2019, type = \"classes\")\n\n\n\n\n\n\n\n\nTo display the actual tiles of landcover types, as well as the official colors of Phnom Penh Landcover nomenclature (available here), you can proceed as follows.\n\nclass_name <- c(\n \"Roads\",\n \"Built-up areas\",\n \"Water Bodies and rivers\",\n \"Wetlands\",\n \"Dry bare area\",\n \"Bare crop fields\",\n \"Low vegetation areas\",\n \"High vegetation areas\",\n \"Forested areas\")\n\nclass_color <- c(\"#070401\", \"#c84639\", \"#1398eb\",\"#8bc2c2\",\n \"#dc7b34\", \"#a6bd5f\",\"#e8e8e8\", \"#4fb040\", \"#35741f\")\nplot(lulc_2019,\n type = \"classes\",\n levels = class_name,\n col = class_color,\n plg = list(cex = 0.7),\n mar = c(3.1, 3.1, 2.1, 10) #The margin are (bottom, left, top, right) respectively\n )"
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"text": "4.4 Change to the study area\n\n4.4.1 (Re)projections\nTo modify the projection system of a raster, use the function project(). It is then necessary to indicate the method for estimating the new cell values.\n\n\n\nSource : Centre Canadien de Télédétection\n\n\nFour interpolation methods are available:\n\nnear : nearest neighbor, fast and default method for qualitative data;\n\nbilinear : bilinear interpolation. Default method for quantitative data;\n\ncubic : cubic interpolation;\n\ncubicspline : cubic spline interpolation.\n\n\n# Re-project data \n\nelevation_utm = project(x = elevation, y = \"EPSG:32648\", method = \"bilinear\") #from WGS84(EPSG:4326) to UTM zone48N(EPSG:32648) \nlulc_2019_utm = project(x = lulc_2019, y = \"EPSG:32648\", method = \"near\") #keep original projection: UTM zone48N(EPSG:32648)\n\n\n\n\n\n\n\n\n\n\n\n\n4.4.2 Crop\nClipping a raster to the extent of another object SpatVector or SpatRaster is achievable with the crop().\n\n\n\n\n\n\n\n\n\n\n\nSource : (Racine 2016)\n\n\n\nImport vector data of (municipal divisions) using function vect. This data will be stored in an SpatVector object.\n\ndistrict <- vect(\"data_cambodia/cambodia.gpkg\", layer=\"district\")\n\nExtraction of district boundaries of Thma Bang district (ADM2_PCODE : KH0907).\n\nthma_bang <- subset(district, district$ADM2_PCODE == \"KH0907\") \n\nUsing the function crop(), Both data layers must be in the same projection.\n\ncrop_thma_bang <- crop(elevation_utm, thma_bang)\n\nplot(crop_thma_bang)\nplot(thma_bang, add=TRUE)\n\n\n\n\n\n\n\n\n\n\n4.4.3 Mask\nTo display only the values of a raster contained in a polygon, use the function mask().\n\n\n\nSource : (Racine 2016)\n\n\nCreation of a mask on the crop_thma_bang raster to the municipal limits (polygon) of Thma Bang district.\n\nmask_thma_bang <- mask(crop_thma_bang, thma_bang)\n\nplot(mask_thma_bang)\nplot(thma_bang, add = TRUE)\n\n\n\n\n\n\n\n\n\n\n4.4.4 Aggregation and disaggregation\nResampling a raster to a different resolution is done in two steps.\n\n\n\n\n\n\n1\n\n\n\n\n\n\n\n2\n\n\n\n\n\n\n\n3\n\n\n\n\n\n\nSource : (Racine 2016)\n\n\n\nDisplay the resolution of a raster with the function res().\n\nres(elevation_utm) #check cell size\n\n[1] 91.19475 91.19475\n\n\nCreate a grid with the same extent, then decrease the spatial resolution (larger cells).\n\nelevation_LowerGrid <- elevation_utm\n# elevation_HigherGrid <- elevation_utm\n\nres(elevation_LowerGrid) <- 1000 #cells size = 1000 meter\n# res(elevation_HigherGrid) <- 10 #cells size = 10 meter\n\nelevation_LowerGrid\n\nclass : SpatRaster \ndimensions : 484, 589, 1 (nrow, ncol, nlyr)\nresolution : 1000, 1000 (x, y)\nextent : 203586.3, 792586.3, 1142954, 1626954 (xmin, xmax, ymin, ymax)\ncoord. ref. : WGS 84 / UTM zone 48N (EPSG:32648) \n\n\nThe function resample() allows to resample the atarting values in the new spatial resolution. Several resampling methods are available (cf. partie 5.4.1).\n\nelevation_LowerGrid <- resample(elevation_utm, \n elevation_LowerGrid, \n method = \"bilinear\") \n\nplot(elevation_LowerGrid, \n main=\"Cell size = 1000m\\nBilinear resampling method\")\n\n\n\n\n\n\n\n\n\n\n4.4.5 Raster fusion\nMerge multiple objects SpatRaster into one with merge() or mosaic().\n\n\n\nSource : https://desktop.arcgis.com/fr/arcmap/10.3/manage-data/raster-and-images/what-is-a-mosaic.htm\n\n\nAfter cutting the elevation raster by the municipal boundary of Thma Bang district (cf partie 5.4.2), we do the same thing for the neighboring municipality of Phnum Kravanh district.\n\nphnum_kravanh <- subset(district, district$ADM2_PCODE == \"KH1504\") # Extraction of the municipal boundaries of Phnum Kravanh district\n\ncrop_phnum_kravanh <- crop(elevation_utm, phnum_kravanh) #clipping the elevation raster according to district boundaries\n\nThe crop_thma_bang and crop_phnum_kravanh elevation raster overlap spatially:\n\n\n\n\n\n\n\n\n\nThe difference between the functions merge() and mosiac() relates to values of the overlapping cells. The function mosaic() calculate the average value while merge() holding the value of the object SpaRaster called n the function.\n\n#in this example, merge() and mosaic() give the same result\nmerge_raster <- merge(crop_thma_bang, crop_phnum_kravanh) \nmosaic_raster <- mosaic(crop_thma_bang, crop_phnum_kravanh)\n\nplot(merge_raster)\n\n\n\n\n\n\n\n# plot(mosaic_raster)\n\n\n\n4.4.6 Segregate\nDecompose a raster by value (or modality) into different rasterlayers with the function segregate.\n\nlulc_2019_class <- segregate(lulc_2019, keep=TRUE, other=NA) #creating a raster layer by modality\nplot(lulc_2019_class)"
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"text": "4.5 Map Algebra\nMap algebra is classified into four groups of operation (Tomlin 1990):\n\nLocal : operation by cell, on one or more layers;\n\nFocal : neighborhood operation (surrounding cells);\n\nZonal : to summarize the matrix values for certain zones, usually irregular;\nGlobal : to summarize the matrix values of one or more matrices.\n\n\n\n\nSource : (Li 2009)\n\n\n\n4.5.1 Local operations\n\n\n\nSource : (Mennis 2015)\n\n\n\n4.5.1.1 Value replacement\n\nelevation_utm[elevation_utm[[1]]== -9999] <- NA #replaces -9999 values with NA\n\nelevation_utm[elevation_utm < 1500] <- NA #Replace values < 1500 with NA\n\n\nelevation_utm[is.na(elevation_utm)] <- 0 #replace NA values with 0\n\n\n\n4.5.1.2 Operation on each cell\n\nelevation_1000 <- elevation_utm + 1000 # Adding 1000 to the value of each cell\n\nelevation_median <- elevation_utm - global(elevation_utm, median)[[1]] # Removed median elevation to each cell's value\n\n\n\n\n\n\n\n\n\n\n\n\n4.5.1.3 Reclassification\nReclassifying raster values can be used to discretize quantitative data as well as to categorize qualitative categories.\n\nreclassif <- matrix(c(1, 2, 1, \n 2, 4, 2,\n 4, 6, 3,\n 6, 9, 4), \n ncol = 3, byrow = TRUE)\n\nValues between 1 and 2 will be replaced by the value 1.\nValues between 3 and 4 will be replaced by the value 2.\nValues between 5 and 6 will be replaced by the value 3. Values between 7 and 9 will be replaced by the value 4.\n…\n\nreclassif\n\n [,1] [,2] [,3]\n[1,] 1 2 1\n[2,] 2 4 2\n[3,] 4 6 3\n[4,] 6 9 4\n\n\nThe function classify() allows you to perform the reclassification.\n\nlulc_2019_reclass <- classify(lulc_2019, rcl = reclassif)\nplot(lulc_2019_reclass, type =\"classes\")\n\n\n\n\nDisplay with the official titles and colors of the different categories.\n\nplot(lulc_2019_reclass, \n type =\"classes\", \n levels=c(\"Urban areas\",\n \"Water body\",\n \"Bare areas\",\n \"Vegetation areas\"),\n col=c(\"#E6004D\",\n \"#00BFFF\",\n \"#D3D3D3\", \n \"#32CD32\"),\n mar=c(3, 1.5, 1, 11))\n\n\n\n\n\n\n\n\n\n\n4.5.1.4 Operation on several layers (ex: NDVI)\nIt is possible to calculate the value of a cell from its values stored in different layers of an object SpatRaster.\nPerhaps the most common example is the calculation of the Normalized Vegetation Index (NDVI). For each cell, a value is calculated from two layers of raster from a multispectral satellite image.\n\n# Import d'une image satellite multispectrale\nsentinel2a <- rast(\"data_cambodia/Sentinel2A.tif\")\n\nThis multispectral satellite image (10m resolution) dated 25/02/2020, was produced by Sentinel-2 satellite and was retrieved from plateforme Copernicus Open Access Hub. An extraction of Red and near infrared spectral bands, centered on the Phnom Penh city, was then carried out.\n\nplot(sentinel2a)\n\n\n\n\n\n\n\n\nTo lighten the code, we assign the two matrix layers in different SpatRaster objects.\n\nB04_Red <- sentinel2a[[1]] #spectral band Red\n\nB08_NIR <-sentinel2a[[2]] #spectral band near infrared\n\nFrom these two raster objects , we can calculate the normalized vegetation index:\n\\[{NDVI}=\\frac{\\mathrm{NIR} - \\mathrm{Red}} {\\mathrm{NIR} + \\mathrm{Red}}\\]\n\nraster_NDVI <- (B08_NIR - B04_Red ) / (B08_NIR + B04_Red )\n\nplot(raster_NDVI)\n\n\n\n\n\n\n\n\nThe higher the values (close to 1), the denser the vegetation.\n\n\n\n4.5.2 Focal operations\n\n\n\nSource : (Mennis 2015)\n\n\nFocal analysis conisders a cell plus its direct neighbors in contiguous and symmetrical (neighborhood operations). Most often, the value of the output cell is the result of a block of 3 x 3 (odd number) input cells.\nThe first step is to build a matrix that determines the block of cells that will be considered around each cell.\n\n# 5 x 5 matrix, where each cell has the same weight\nmon_focal <- matrix(1, nrow = 5, ncol = 5)\nmon_focal\n\n [,1] [,2] [,3] [,4] [,5]\n[1,] 1 1 1 1 1\n[2,] 1 1 1 1 1\n[3,] 1 1 1 1 1\n[4,] 1 1 1 1 1\n[5,] 1 1 1 1 1\n\n\nThe function focal() Then allows you to perform the desired analysis. For example: calculating the average of the values of all contiguous cells, for each cell in the raster.\n\nelevation_LowerGrid_mean <- focal(elevation_LowerGrid, \n w = mon_focal, \n fun = mean)\n\n\n\n\n\n\n\n\n\n\n\n4.5.2.1 Focal operations for elevation rasters\nThe function terrain() allows to perform focal analyzes specific to elevation rasters. Six operations are available:\n\nslope = calculation of the slope or degree of inclination of the surface;\n\naspect = calculate slope orientation;\n\nroughness = calculate of the variability or irregularity of the elevation;\n\nTPI = calculation of the index of topgraphic positions;\n\nTRI = elevation variability index calculation;\n\nflowdir = calculation of the water flow direction.\n\nExample with calculation of slopes(slope).\n\n#slope calculation\nslope <- terrain(elevation_utm, \"slope\", \n neighbors = 8, #8 (or 4) cells around taken into account\n unit = \"degrees\") #Output unit\n\nplot(slope) #Inclination of the slopes, in degrees\n\n\n\n\n\n\n\n\n\n\n\n4.5.3 Global operations\n\n\n\nSource : https://gisgeography.com/map-algebra-global-zonal-focal-local\n\n\nGlobal operation are used to summarize the matrix values of one or more matrices.\n\nglobal(elevation_utm, fun = \"mean\") #average values\n\n mean\nAltitude 80.01082\n\n\n\nglobal(elevation_utm, fun = \"sd\") #standard deviation\n\n sd\nAltitude 155.885\n\n\n\nfreq(lulc_2019_reclass) #frequency\n\n layer value count\n1 1 1 47485325\n2 1 2 13656289\n3 1 3 14880961\n4 1 4 37194979\n\ntable(lulc_2019_reclass[]) #contingency table\n\n\n 1 2 3 4 \n47485325 13656289 14880961 37194979 \n\n\nStatistical representations that summarize matrix information.\n\nhist(elevation_utm) #histogram\n\nWarning: [hist] a sample of3% of the cells was used\n\n\n\n\n\n\n\n\ndensity(elevation_utm) #density\n\n\n\n\n\n\n\n\n\n\n4.5.4 Zonal operation\n\n\n\nSource : (Mennis 2015)\n\n\nThe zonal operation make it possible to summarize the matrix values of certain zones (group of contiguous cells in space or in value).\n\n4.5.4.1 Zonal operation on an extraction\nAll global operations can be performed on an extraction of cells resulting from the functions crop(), mask(), segregate()…\nExample: average elevation for the city of Thma Bang district (cf partie 5.4.3).\n\n# Average value of the \"mask\" raster over Thma Bang district\nglobal(mask_thma_bang, fun = \"mean\", na.rm=TRUE)\n\n mean\nAltitude 584.7703\n\n\n\n\n4.5.4.2 Zonal operation from a vector layer\nThe function extract() allows you to extract and manipulate the values of cells that intersect vector data.\nExample from polygons:\n\n# Average elevation for each polygon (district)?\nelevation_by_dist <- extract(elevation_LowerGrid, district, fun=mean)\nhead(elevation_by_dist, 10)\n\n ID Altitude\n1 1 8.953352\n2 2 196.422240\n3 3 23.453937\n4 4 3.973118\n5 5 29.545801\n6 6 41.579593\n7 7 50.162749\n8 8 85.128777\n9 9 269.068091\n10 10 8.439041\n\n\n\n\n4.5.4.3 Zonal operation from raster\nZonal operation can be performed by area bounded by the categorical values of a second raster. For this, the two raster must have exaclty the same extent and the same resolution.\n\n#create a second raster with same resolution and extent as \"elevation_clip\"\nelevation_clip <- rast(\"data_cambodia/elevation_clip.tif\")\nelevation_clip_utm <- project(x = elevation_clip, y = \"EPSG:32648\", method = \"bilinear\")\nsecond_raster_CLC <- rast(elevation_clip_utm)\n\n#resampling of lulc_2019_reclass \nsecond_raster_CLC <- resample(lulc_2019_reclass, second_raster_CLC, method = \"near\") \n \n#added a variable name for the second raster\nnames(second_raster_CLC) <- \"lulc_2019_reclass_resample\"\n\n\n\n\n\n\n\n\n\n\nCalculation of the average elevation for the different areas of the second raster.\n\n#average elevation for each area of the \"second_raster\"\nzonal(elevation_clip_utm, second_raster_CLC , \"mean\", na.rm=TRUE)\n\n lulc_2019_reclass_resample elevation_clip\n1 1 12.83846\n2 2 8.31809\n3 3 11.41178\n4 4 11.93546"
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"text": "4.6 Transformation and conversion\n\n4.6.1 Rasterization\nConvert polygons to raster format.\n\nchamkarmon = subset(district, district$ADM2_PCODE ==\"KH1201\") \nraster_district <- rasterize(x = chamkarmon, y = elevation_clip_utm)\n\n\nplot(raster_district)\n\n\n\n\n\n\n\n\nConvert points to raster format\n\n#rasterization of the centroids of the municipalities\nraster_dist_centroid <- rasterize(x = centroids(district), \n y = elevation_clip_utm, fun=sum)\nplot(raster_dist_centroid, col = \"red\")\nplot(district, add =TRUE)\n\n\n\n\nConvert lines in raster format\n\n#rasterization of municipal boundaries\nraster_dist_line <- rasterize(x = as.lines(district), y = elevation_clip_utm, fun=sum)\n\n\nplot(raster_dist_line)\n\n\n\n\n\n\n4.6.2 Vectorisation\nTransform a raster to vector polygons.\n\npolygon_elevation <- as.polygons(elevation_clip_utm)\n\n\nplot(polygon_elevation, y = 1, border=\"white\")\n\n\n\n\nTransform a raster to vector points.\n\npoints_elevation <- as.points(elevation_clip_utm)\n\n\nplot(points_elevation, y = 1, cex = 0.3)\n\n\n\n\nTransform a raster into vector lines.\n\nlines_elevation <- as.lines(elevation_clip_utm)\n\n\nplot(lines_elevation)\n\n\n\n\n\n\n4.6.3 terra, raster, sf, stars…\nReference packages for manipulating spatial data all rely o their own object class. It is sometimes necessary to convert these objects from one class to another class to take advance of all the features offered by these different packages.\nConversion functions for raster data:\n\n\n\nFROM/TO\nraster\nterra\nstars\n\n\n\n\nraster\n\nrast()\nst_as_stars()\n\n\nterra\nraster()\n\nst_as_stars()\n\n\nstars\nraster()\nas(x, ‘Raster’) + rast()\n\n\n\n\nConversion functions for vector data:\n\n\n\nFROM/TO\nsf\nsp\nterra\n\n\n\n\nsf\n\nas(x, ‘Spatial’)\nvect()\n\n\nsp\nst_as_sf()\n\nvect()\n\n\nterra\nst_as_sf()\nas(x, ‘Spatial’)\n\n\n\n\n\n\n\n\nHijmans, Robert J. 2022. “Terra: Spatial Data Analysis.” https://CRAN.R-project.org/package=terra.\n\n\nLi, Xingong. 2009. “Map Algebra and Beyond : 1. Map Algebra for Scalar Fields.” https://slideplayer.com/slide/5822638/.\n\n\nMadelin, Malika. 2021. “Analyse d’images Raster (Et Télédétection).” https://mmadelin.github.io/sigr2021/SIGR2021_raster_MM.html.\n\n\nMennis, Jeremy. 2015. “Fundamentals of GIS : Raster Operations.” https://cupdf.com/document/gus-0262-fundamentals-of-gis-lecture-presentation-7-raster-operations-jeremy.html.\n\n\nNowosad, Jakub. 2021. “Image Processing and All Things Raster.” https://nowosad.github.io/SIGR2021/workshop2/workshop2.html.\n\n\nRacine, Etienne B. 2016. “The Visual Raster Cheat Sheet.” https://rpubs.com/etiennebr/visualraster.\n\n\nTomlin, C. Dana. 1990. Geographic Information Systems and Cartographic Modeling. Prentice Hall."
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"text": "The fonction mf_map() is the central function of the package mapsf (Giraud 2022a). It makes it possible to carry out most of the usual representations in cartography. These main arguments are:\n\nx, an sf object ;\nvar, the name of variable to present ;\ntype, the type of presentation.\n\n\n\nThe following lines import the spatial information layers located in the geopackage cambodia.gpkg file.\n\nlibrary(sf)\n\n#Import Cambodia country border\ncountry = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"country\", quiet = TRUE)\n#Import provincial administrative border of Cambodia\neducation = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"education\", quiet = TRUE)\n#Import district administrative border of Cambodia\ndistrict = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\", quiet = TRUE)\n#Import roads data in Cambodia\nroad = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"road\", quiet = TRUE)\n#Import health center data in Cambodia\nhospital = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"hospital\", quiet = TRUE)\n\n\n\n\nWithout using types specification, the function mf_map() simply display the background map.\n\nlibrary(mapsf)\n\nmf_map(x = district, border = \"white\")\nmf_map(x = country,lwd = 2, col = NA, add = TRUE)\nmf_map(x = road, lwd = .5, col = \"ivory4\", add = TRUE)\nmf_map(x = hospital, pch = 20, cex = 1, col = \"#FE9A2E\", add = TRUE) \n\n\n\n\n\n\n\nProportional symbol maps are used to represent inventory variables (absolute quantitative variables, sum and average make sense). The function mf_map(..., type = \"prop\") proposes this representation.\n\n#District\nmf_map(x = district) \n\n# Proportional symbol \nmf_map(\n x = district, \n var = \"T_POP\",\n val_max = 700000,\n type = \"prop\",\n col = \"#148F77\", \n leg_title = \"Population 2019\"\n)\n\n# Title\nmf_title(\"Distribution of population in provincial level\")\n\n\n\n\n\n\nIt is possible to fix the dimensions of the largest symbol corresponding to a certain value with the arguments inches and val_max. We can use construct maps with comparable proportional symbols.\n\npar(mfrow = c(1,2)) #Displaying two maps facing each other\n\n#district\nmf_map(x = district, border = \"grey90\", lwd = .5) \n# Add male Population\nmf_map(\n x = district, \n var = \"Male\", \n type = \"prop\",\n col = \"#1F618D\",\n inches = 0.2, \n val_max = 300000, \n leg_title = \"Male\", \n leg_val_cex = 0.5,\n)\nmf_title(\"Male Population by Distict\") #Adding map title\n\n#district\nmf_map(x = district, border = \"grey90\", lwd = .5) \n# Add female Population\nmf_map(\n x = district, \n var = \"Female\", \n type = \"prop\",\n col = \"#E74C3C\",\n inches = 0.2, \n val_max = 300000, \n leg_title =\"Female\", \n leg_val_cex = 0.5\n)\nmf_title(\"Female Population by Distict\") #Adding map title\n\n\n\n\nHere we have displayed two maps facing each other, see the point Displaying several maps on the same figure for more details.\n\n\n\n\nChoropleth maps are used to represent ratio variables (relative quantitative variables, mean has meaning, sum has no meaning).\nFor this type of representation, you must first:\n\nchoose a discretization method to transform a continuous statistical series into classes defined by intervals,\nchoose a number of classes,\nchoose a color palette.\n\nThe function mf_map(…, type = “choro”)makes it possible to create choroplete maps. The arguments nbreaks and breaks are used to parameterize the discretizations, and the function mf_get_breaks() makes it possible to work on the discretizations outside the function mf_map(). Similarly, the argument palis used to fill in a color palette, but several functions can be used to set the palettes apart from the (mf_get_pal…) function.\n\n# Population density (inhabitants/km2) using the sf::st_area() function\ndistrict$DENS <- 1e6 * district$T_POP / as.numeric(st_area(district)) #Calculate population density \nmf_map(\n x = district,\n var = \"DENS\",\n type = \"choro\",\n breaks = \"quantile\",\n pal = \"BuGn\",\n lwd = 1,\n leg_title = \"Distribution of population\\n(inhabitants per km2)\", \n leg_val_rnd = 0\n)\nmf_title(\"Distribution of the population in (2019)\")\n\n\n\n\n\ncases = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"cases\", quiet = TRUE)\ncases = subset(cases, Disease == \"W fever\")\npopulation <- read.csv(\"data_cambodia/khm_admpop_adm2_2016_v2.csv\")\npopulation <- population[, c(\"ADM2_PCODE\", \"T_TL\")]\n# Remove commas\npopulation$T_TL <- as.numeric(gsub(\",\",\"\",population$T_TL))\ndistrict$cases <- lengths(st_intersects(district, cases))\ndistrict <- merge(district,\n population,\n by = \"ADM2_PCODE\")\ndistrict$incidence <- district$cases / district$T_TL * 100000\n\nmf_map(x = district,\n var = \"incidence\",\n type = \"choro\",\n leg_title = \"Incidence (per 100 000)\")\nmf_layout(title = \"Incidence of W Fever in Cambodia\")\n\n\n\n\n\n\nThe fonction mf_get_breaks() provides the methods of discretization of classic variables: quantiles, average/standard deviation, equal amplitudes, nested averages, Fisher-Jenks, geometric, etc.\n\neducation$enrol_g_pct = 100 * education$enrol_girl/education$t_enrol #Calculate percentage of enrolled girl student\n\nd1 = mf_get_breaks(education$enrol_g_pct, nbreaks = 6, breaks = \"equal\", freq = TRUE)\nd2 = mf_get_breaks(education$enrol_g_pct, nbreaks = 6, breaks = \"quantile\")\nd3 = mf_get_breaks(education$enrol_g_pct, nbreaks = 6, breaks = \"geom\")\nd4 = mf_get_breaks(education$enrol_g_pct, breaks = \"msd\", central = FALSE)\n\n\n\n\n\n\n\n\n\nThe argument pal de mf_map() is dedicated to choosing a color palette. The palettes provided by the function hcl.colors() can be used directly.\n\nmf_map(x = education, var = \"enrol_g_pct\", type = \"choro\",\n breaks = d3, pal = \"Reds 3\")\n\n\n\n\n\n\n\n\n\nThe fonction mf_get_pal() allows you to build a color palette. This function is especially useful for creating balanced asymmetrical diverging palettes.\n\nmypal <- mf_get_pal(n = c(4,6), palette = c(\"Burg\", \"Teal\"))\nimage(1:10, 1, as.matrix(1:10), col=mypal, xlab = \"\", ylab = \"\", xaxt = \"n\",\n yaxt = \"n\",bty = \"n\")\n\n\n\n\n\n\n\nIt is possible to use this mode of presentation in specific implementation also.\n\ndist_c <- st_centroid(district)\nmf_map(district)\nmf_map(\n x = dist_c,\n var = \"DENS\",\n type = \"choro\",\n breaks = \"quantile\",\n nbreaks = 5,\n pal = \"PuRd\",\n pch = 23,\n cex = 1.5,\n border = \"white\",\n lwd = .7,\n leg_pos = \"topleft\",\n leg_title = \"Distribution of population\\n(inhabitants per km2)\", \n leg_val_rnd = 0, \n add = TRUE\n)\nmf_title(\"Distribution of population in (2019)\")\n\n\n\n\n\n\n\n\nTypology maps are used to represent qualitative variables. The function mf_map(..., type = \"typo\") proposes this representation.\n\nmf_map(\n x = district,\n var=\"Status\",\n type = \"typo\",\n pal = c('#E8F9FD','#FF7396','#E4BAD4','#FFE3FE'),\n lwd = .7,\n leg_title = \"\"\n)\nmf_title(\"Administrative status by size of area\")\n\n\n\n\n\n\nThe argument val_order is used to order the categories in the\n\nmf_map(\n x = district,\n var=\"Status\",\n type = \"typo\",\n pal = c('#E8F9FD','#FF7396','#E4BAD4','#FFE3FE'),\n val_order = c(\"1st largest district\", \"2nd largest district\", \"3rd largest district\",\"<4500km2\"),\n lwd = .7,\n leg_title = \"\"\n)\nmf_title(\"Administrative status by size of area\")\n\n\n\n\n\n\n\nWhen the implantation of the layer is punctual, symbols are used to carry the colors of the typology.\n\n#extract centroid point of the district\ndist_ctr <- st_centroid(district[district$Status != \"<4500km2\", ])\nmf_map(district)\nmf_map(\n x = dist_ctr,\n var = \"Status\",\n type = \"typo\",\n cex = 2,\n pch = 22,\n pal = c('#FF7396','#E4BAD4','#FFE3FE'),\n leg_title = \"\",\n leg_pos = \"bottomright\",\n add = TRUE\n)\nmf_title(\"Administrative status by size of area\")\n\n\n\n\n\n\n\n\n#Selection of roads that intersect the city of Siem Reap\npp <- district[district$ADM1_EN == \"Phnom Penh\", ]\nroad_pp <- road[st_intersects(x = road, y = pp, sparse = FALSE), ]\nmf_map(pp)\nmf_map(\n x = road_pp,\n var = \"fclass\",\n type = \"typo\",\n lwd = 1.2,\n pal = mf_get_pal(n = 6, \"Tropic\"),\n leg_title = \"Types of road\",\n leg_pos = \"topright\",\n leg_frame = T,\n add = TRUE\n)\nmf_title(\"Administrative status\")\n\n\n\n\n\n\n\n\nThe function mf_map(..., var = c(\"var1\", \"var2\"), type = \"prop_choro\") represents proportional symbols whose areas are proportional to the values of one variable and whose color is based on the discretization of a second variable. The function uses the arguments of the functions mf_map(..., type = \"prop\") and mf_map(..., type = \"choro\").\n\nmf_map(x = district)\nmf_map(\n x = district,\n var = c(\"T_POP\", \"DENS\"),\n val_max = 500000,\n type = \"prop_choro\",\n border = \"grey60\",\n lwd = 0.5,\n leg_pos = c(\"bottomright\", \"bottomleft\"),\n leg_title = c(\"Population\", \"Density of\\n population\\n(inhabitants per km2)\"),\n breaks = \"q6\",\n pal = \"Blues 3\",\n leg_val_rnd = c(0,1))\nmf_title(\"Population\")\n\n\n\n\n\n\n\nThe function mf_map(..., var = c(\"var1\", \"var2\"), type = \"prop_typo\") represents proportional symbols whose areas are proportional to the values of one variable and whose color is based on the discretization of a second variable. The function uses the arguments of the mf_map(..., type = \"prop\") and function mf_map(..., type = \"typo\").\n\nmf_map(x = district)\nmf_map(\n x = district,\n var = c(\"Area.Km2.\", \"Status\"),\n type = \"prop_typo\",\n pal = c('#E8F9FD','#FF7396','#E4BAD4','#FFE3FE'),\n val_order = c(\"<4500km2\",\"1st largest district\", \"2nd largest district\", \"3rd largest district\"),\n leg_pos = c(\"bottomleft\",\"topleft\"),\n leg_title = c(\"Population\\n(2019)\",\n \"Statut administratif\"),\n)\nmf_title(\"Population\")"
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"text": "5.2 Layout\nTo be finalized, a thematic map must contain certain additional elements such as: title, author, source, scale, orientation…\n\n5.2.1 Example data\nThe following lines import the spatial information layers located in the geopackage lot46.gpkg file.\n\nlibrary(sf)\ncountry = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"country\", quiet = TRUE) #Import Cambodia country border\neducation = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"education\", quiet = TRUE) #Import provincial administrative border of Cambodia\ndistrict = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\", quiet = TRUE) #Import district administrative border of Cambodia\nroad = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"road\", quiet = TRUE) #Import roads data in Cambodia\nhospital = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"hospital\", quiet = TRUE) #Import hospital data in Cambodia\ncases = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"cases\", quiet = TRUE) #Import example data of fever_cases in Cambodia\n\n\n\n5.2.2 Themes\nThe function mf_theme() defines a cartographic theme. Using a theme allows you to define several graphic parameters which are then applied to the maps created with mapsf. These parameters are: the map margins, the main color, the background color, the position and the aspect of the title. A theme can also be defined with the mf_init() and function mf_export().\n\n5.2.2.1 Use a predefined theme\nA series of predefined themes are available by default (see ?mf_theme).\n\nlibrary(mapsf)\n# use of a background color for the figure, to see the use of margin\nopar <- par(mfrow = c(2,2))\n# Using a predefined theme\nmf_theme(\"default\")\nmf_map(district)\nmf_title(\"Theme : 'default'\")\n\nmf_theme(\"darkula\")\nmf_map(district)\nmf_title(\"Theme : 'darkula'\")\n\nmf_theme(\"candy\")\nmf_map(district)\nmf_title(\"Theme : 'candy'\")\n\nmf_theme(\"nevermind\")\nmf_map(district)\nmf_title(\"Theme : 'nevermind'\")\npar(opar)\n\n\n\n\n\n\n5.2.2.2 Modify an existing theme\nIt is possible to modify an existing theme. In this example, we are using the “default” theme and modifying a few settings.\n\nlibrary(mapsf)\nopar <- par(mfrow = c(1,2))\nmf_theme(\"default\")\nmf_map(district)\nmf_title(\"default\")\n\nmf_theme(\"default\", tab = FALSE, font = 4, bg = \"grey60\", pos = \"center\")\nmf_map(district)\nmf_title(\"modified default\")\npar(opar)\n\n\n\n\n\n\n5.2.2.3 Create a theme\nIt is also possible to create a theme.\n\nmf_theme(\n bg = \"lightblue\", # background color\n fg = \"tomato1\", # main color\n mar = c(1,0,1.5,0), # margin\n tab = FALSE, # \"tab\" style for the title\n inner = FALSE, # title inside or outside of map area\n line = 1.5, # space dedicated to title\n pos = \"center\", # heading position\n cex = 1.5, # title size\n font = 2 # font types for title\n)\nmf_map(district)\nmf_title(\"New theme\")\n\n\n\n\n\n\n\n5.2.3 Titles\nThe function mf_title() adds a title to a map.\n\nmf_theme(\"default\")\nmf_map(district)\nmf_title(\"Map title\")\n\n\n\n\nIt is possible to customize the appearance of the title\n\nmf_map(district)\nmf_title(\n txt = \"Map title\", \n pos = \"center\", \n tab = FALSE, \n bg = \"tomato3\", \n fg = \"lightblue\", \n cex = 1.5, \n line = 1.7, \n font = 1, \n inner = FALSE\n)\n\n\n\n\n\n\n5.2.4 Arrow orientation\nThe function mf_arrow() allows you to choose the position and aspect of orientation arrow.\n\nmf_map(district)\nmf_arrow()\n\n\n\n\n\n\n5.2.5 Scale\nThe function mf_scale() allows you to choose the position and the aspect of the scale.\n\nmf_map(district)\nmf_scale(\n size = 60,\n lwd = 1,\n cex = 0.7\n)\n\n\n\n\n\n\n5.2.6 Credits\nThe function mf_credits() displays a line of credits (sources, author, etc.).\n\nmf_map(district)\nmf_credits(\"IRD\\nInstitut Pasteur du Cambodge, 2022\")\n\n\n\n\n\n\n5.2.7 Complete dressing\nThe function mf_layout() displays all these elements.\n\nmf_map(district)\nmf_layout(\n title = \"Cambodia\",\n credits = \"IRD\\nInstitut Pasteur du Cambodge, 2022\",\n arrow = TRUE\n)\n\n\n\n\n\n\n5.2.8 Annotations\n\nmf_map(district)\nmf_annotation(district[district$ADM2_EN == \"Bakan\",], txt = \"Bakan\", col_txt = \"darkred\", halo = TRUE, cex = 1.5)\n\n\n\n\n\n\n5.2.9 Legends\n\nmf_map(district)\nmf_legend(\n type = \"prop\", \n val = c(1000,500,200,10), \n inches = .2, \n title = \"Population\", \n pos = \"topleft\"\n)\nmf_legend(\n type = \"choro\", \n val = c(0,10,20,30,40),\n pal = \"Greens\", \n pos = \"bottomright\", \n val_rnd = 0\n)\n\n\n\n\n\n\n5.2.10 Labels\nThe function mf_label() is dedicated to displaying labels.\n\ndist_selected <- district[st_intersects(district, district[district$ADM2_EN == \"Bakan\", ], sparse = F), ]\n\nmf_map(dist_selected)\nmf_label(\n x = dist_selected,\n var = \"ADM2_EN\",\n col= \"darkgreen\",\n halo = TRUE,\n overlap = FALSE, \n lines = FALSE\n)\nmf_scale()\n\n\n\n\nThe argument halo = TRUE allows to display a slight halo around the labels and the argument overlap = FALSE allows to create non-overlapping labels.\n\n\n5.2.11 Center the map on a region\nThe function mf_init() allows you to initialize a map by centering it on a spatial object.\n\nmf_init(x = dist_selected)\nmf_map(district, add = TRUE)\nmf_map(dist_selected, col = NA, border = \"#29a3a3\", lwd = 2, add = TRUE)\n\n\n\n\n\n\n5.2.12 Displaying several maps on the sam figure\nHere you have to use mfrow of the function par(). The first digit represents the number of of rows and second the number of columns.\n\n# define the figure layout (1 row, 2 columns)\npar(mfrow = c(1, 2))\n\n# first map\nmf_map(district)\nmf_map(district, \"Male\", \"prop\", val_max = 300000)\nmf_title(\"Population, male\")\n\n# second map\nmf_map(district)\nmf_map(district, \"Female\", \"prop\", val_max = 300000)\nmf_title(\"Population, female\")\n\n\n\n\n\n\n5.2.13 Exporting maps\nIt is quite difficult to export figures (maps or others) whose height/width ratio is satisfactory. The default ratio of figures in png format is 1 (480x480 pixels):\n\ndist_filter <- district[district$ADM2_PCODE == \"KH0808\", ]\npng(\"img/dist_filter_1.png\")\nmf_map(dist_filter)\nmf_title(\"Filtered district\")\ndev.off()\n\n\n\n\n\n\nOn this map a lot of space is lost to the left and right of the district.\nThe function mf_export() allows exports of maps whose height/width ratio is controlled and corresponds to that of a spatial object.\n\nmf_export(dist_filter, \"img/dist_filter_2.png\", width = 480)\nmf_map(dist_filter)\nmf_title(\"Filtered district\")\ndev.off()\n\n\n\n\n\n\nThe extent of this map is exactly that of the displayed region.\n\n\n5.2.14 Adding an image to a map\nThis can be useful for adding a logo, a pictograph. The function readPNG() of package png allows the additional images on the figure.\n\nmf_theme(\"default\", mar = c(0,0,0,0))\nlibrary(png)\n\nlogo <- readPNG(\"img/ird_logo.png\") #Import image\npp <- dim(logo)[2:1]*200 #Image dimension in map unit (width and height of the original image)\n\n#The upper left corner of the department's bounding box\nxy <- st_bbox(district)[c(1,4)]\nmf_map(district, col = \"#D1914D\", border = \"white\")\nrasterImage(\n image = logo,\n xleft = xy[1] ,\n ybottom = xy[2] - pp[2],\n xright = xy[1] + pp[1],\n ytop = xy[2]\n)\n\n\n\n\n\n\n5.2.15 Place an item precisely on the map\nThe function locator() allows clicking on the figure and obtaining the coordinate of a point in the coordinate system of the figure (of the map).\n\n# locator(1) # click to get coordinate on map\n# points(locator(1)) # click to plot point on map\n# text(locator(1), # click to place the item on map\n# labels =\"Located any texts on map\", \n# adj = c(0,0))\n\n\nVideo\nlocator()peut être utilisée sur la plupart des graphiques (pas ceux produits avec ggplot2).\n\n\n\n\n\n\nHow to interactively position legends and layout elements on a map with cartography\n\n\n\n\n\n5.2.16 Add shading to a layer\nThe function mf_shadow() allows to create a shadow to a layer of polygons.\n\nmf_shadow(district)\nmf_map(district, add=TRUE)\n\n\n\n\n\n\n5.2.17 Creating Boxes\nThe function mf_inset_on() allows to start creation a box. You must then “close” the box with mf_inset_off().\n\nmf_init(x = dist_selected, theme = \"agolalight\", expandBB = c(0,.1,0,.5)) \nmf_map(district, add = TRUE)\nmf_map(dist_selected, col = \"tomato4\", border = \"tomato1\", lwd = 2, add = TRUE)\n\n# Cambodia inset box\nmf_inset_on(x = country, pos = \"topright\", cex = .3)\nmf_map(country, lwd = .5, border= \"grey90\")\nmf_map(dist_selected, col = \"tomato4\", border = \"tomato1\", lwd = .5, add = TRUE)\nmf_scale(size = 100, pos = \"bottomleft\", cex = .6, lwd = .5)\nmf_inset_off()\n\n# District inset box\nmf_inset_on(x = district, pos = \"bottomright\", cex = .3)\nmf_map(district, lwd = 0.5, border= \"grey90\")\nmf_map(dist_selected, col = \"tomato4\", border = \"tomato1\", lwd = .5, add = TRUE)\nmf_scale(size = 100, pos = \"bottomright\", cex = .6, lwd = .5)\nmf_inset_off()\n\n# World inset box\nmf_inset_on(x = \"worldmap\", pos = \"topleft\")\nmf_worldmap(dist_selected, land_col = \"#cccccc\", border_col = NA, \n water_col = \"#e3e3e3\", col = \"tomato4\")\n\nmf_inset_off()\nmf_title(\"Bakan district and its surroundings\")\nmf_scale(10, pos = 'bottomleft')"
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"text": "5.3 3D maps\n\n5.3.1 linemap\nThe package linemap (Giraud 2021) allows you to make maps made up of lines.\n\nlibrary(linemap)\nlibrary(mapsf)\nlibrary(sf)\nlibrary(dplyr)\n\npp = st_read(\"data_cambodia/PP.gpkg\", quiet = TRUE) # import Phnom Penh administrative border\npp_pop_dens <- getgrid(x = pp, cellsize =1000, var = \"DENs\") # create population density in grid format (pop density/1km)\n\nmf_init(pp)\n\nlinemap(\n x = pp_pop_dens, \n var = \"DENs\",\n k = 1,\n threshold = 5, \n lwd = 1,\n col = \"ivory1\",\n border = \"ivory4\",\n add = T)\n\nmf_title(\"Phnom Penh Population Density, 2019\")\nmf_credits(\"Humanitarian Data Exchange, 2022\\nunit data:km2\")\n\n\n\n# url = \"https://data.humdata.org/dataset/1803994d-6218-4b98-ac3a-30c7f85c6dbc/resource/f30b0f4b-1c40-45f3-986d-2820375ea8dd/download/health_facility.zip\"\n# health_facility.zip = \"health_facility.zip\"\n# download.file(url, destfile = health_facility.zip)\n# unzip(health_facility.zip) # Unzipped files are in a new folder named Health\n# list.files(path=\"Health\")\n\n\n\n5.3.2 Relief Tanaka\nWe use the tanaka package (Giraud 2022b) which provides a method (Tanaka 1950) used to improve the perception of relief.\n\nlibrary(tanaka)\nlibrary(terra)\n\nrpop <- rast(\"data_cambodia/khm_pd_2019_1km_utm.tif\") # Import population raster data (in UTM)\ndistrict = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\", quiet = TRUE) # Import Cambodian districts layer\ndistrict <- st_transform(district, st_crs(rpop)) # Transform data into the same coordinate system\n\nmat <- focalMat(x = rpop, d = c(1500), type = \"Gauss\") # Raster smoothing\nrpopl <- focal(x = rpop, w = mat, fun = sum, na.rm = TRUE)\n\n# Mapping\ncols <- hcl.colors(8, \"Reds\", alpha = 1, rev = T)[-1]\nmf_theme(\"agolalight\")\nmf_init(district)\ntanaka(x = rpop, breaks = c(0,10,25,50,100,250,500,64265),\n col = cols, add = T, mask = district, legend.pos = \"n\")\nmf_legend(type = \"choro\", pos = \"bottomright\", \n val = c(0,10,25,50,100,250,500,64265), pal = cols,\n bg = \"#EDF4F5\", fg = NA, frame = T, val_rnd = 0,\n title = \"Population\\nper km2\")\nmf_title(\"Population density of Cambodia, 2019\")\nmf_credits(\"Humanitarian Data Exchange, 2022\",\n bg = \"#EDF4F5\")\n\n\n\n\n\n\n\n\n\n\nThe tanaka package"
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"text": "5.4 Cartographic Transformation\n\nclassical anamorphosis is a representation of States(or any cells) by rectangle or any polygons according to a quantities attached to them. (…) We strive to keep the general arrangement of meshes or the silhouette of the continent.”\nBrunet, Ferras, and Théry (1993)\n\n3 types of anamorphoses or cartograms are presented here:\n\nDorling’s cartograms (Dorling 1996)\nNon-contiguous cartograms (Olson 1976)\nContiguous cartograms (Dougenik, Chrisman, and Niemeyer 1985)\n\n\n\n\n\n\n\nA comprehensive course on anamorphoses : Les anamorphoses cartographiques (Lambert 2015).\n\n\n\n\n\n\n\n\n\nMake cartograms with R\n\n\n\nTo make the cartograms we use the package cartogram (Jeworutzki 2020).\n\n5.4.1 Dorling’s cartograms\nThe territories are represented by figures (circles, squares or rectangles) which do not overlap, the surface of which are proportional to a variable. The proportion of the figures are defined according to the starting positions.\n\n\n\n\n\n\n\n\nSpace is quite poorly identified.\nYou can name the circles to get your bearings and/or use the color to make clusters appear and better identify the geographical blocks.\n\n\n\n\n\nThe perception of quantities is very good. The circle sizes are really comarable.\n\n\n\nlibrary(mapsf)\nlibrary(cartogram)\ndistrict <- st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\" , quiet = TRUE)\ndist_dorling <- cartogram_dorling(x = district, weight = \"T_POP\", k = 0.7)\nmf_map(dist_dorling, col = \"#40E0D0\", border= \"white\")\nmf_label(\n x = dist_dorling[order(dist_dorling$T_POP, decreasing = TRUE), ][1:10,], \n var = \"ADM2_EN\",\n overlap = FALSE, \n # show.lines = FALSE,\n halo = TRUE, \n r = 0.15\n)\nmf_title(\"Population of District - Dorling Cartogram\")\n\n\n\n\nThe parameter k allows to vary the expansion factor of the circles.\n\n\n5.4.2 Non-continuous cartograms\nThe size of the polygons is proportional to a variable. The arrangement of the polygons relative to each other is preserved. The shape of the polygons is similar.\n\n\n\n\n\n(Cauvin, Escobar, and Serradj 2013)\n\n\n\nThe topology of the regions is lost.\n\n\n\n\n\nThe converstion of the polygons shape is optimal.\n\n\n\ndist_ncont <- cartogram_ncont(x = district, weight = \"T_POP\", k = 1.2)\nmf_map(district, col = NA, border = \"#FDFEFE\", lwd = 1.5)\nmf_map(dist_ncont, col = \"#20B2AA\", border= \"white\", add = TRUE)\nmf_title(\"Population of District - Non-continuous cartograms\")\n\n\n\n\nThe parameter k allows to vary the expansion of the polygons.\n\n\n5.4.3 Continuous cartograms\nThe size of the polygons is proportional to variable. The arrangement of the polygons relative to each other is preserved. To maintain contiguity, the sape of the polygons is heavily transformed.\n\n\n\n\n\n(Paull and Hennig 2016)\n\n\n\nThe shape of the polygond is strongly distorted.\n\n\n\n\n\nIt is a “real geographical map”: topology and contiguity are preserved.\n\n\n\ndist_ncont <- cartogram_cont(x = district, weight = \"DENs\", maxSizeError = 6)\n\nMean size error for iteration 1: 15.8686749410166\n\n\nMean size error for iteration 2: 12.1107731631101\n\n\nMean size error for iteration 3: 9.98940057337996\n\n\nMean size error for iteration 4: 8.62323208787643\n\n\nMean size error for iteration 5: 7.60706404894655\n\n\nMean size error for iteration 6: 6.83561617758241\n\n\nMean size error for iteration 7: 10.1399490743501\n\n\nMean size error for iteration 8: 5.79418495291592\n\nmf_map(dist_ncont, col = \"#66CDAA\", border= \"white\", add = FALSE)\nmf_title(\"Population of District - Continuous cartograms\")\nmf_inset_on(district, cex = .2, pos = \"bottomleft\")\nmf_map(district, lwd = .5)\nmf_inset_off()\n\n\n\n\n\n\n5.4.4 Stengths and weaknessses of cartograms\ncartograms are cartographic representations perceived as innovative (although the method is 40 years old). These very generalize images capture quantities and gradients well. These are real communication images that provoke, arouse interest, convey a strong message, challenge.\nBut cartograms induce a loss of visual cues (difficult to find one’s country or region on the map), require a reading effort which can be significant and do not make it possible to manage missing data.\n\n\n\n\nBrunet, Roger, Robert Ferras, and Hervé Théry. 1993. Les Mots de La géographie: Dictionnaire Critique. 03) 911 BRU.\n\n\nCauvin, Colette, Francisco Escobar, and Aziz Serradj. 2013. Thematic Cartography, Cartography and the Impact of the Quantitative Revolution. Vol. 2. John Wiley & Sons.\n\n\nDorling, Daniel. 1996. Area Cartograms: Their Use and Creation, Concepts and Techniques in Modern Geography. Vol. 59. CATMOG: Concepts and Techniques in Modern Geography. Institute of British Geographers.\n\n\nDougenik, James A, Nicholas R Chrisman, and Duane R Niemeyer. 1985. “An Algorithm to Construct Continuous Area Cartograms.” The Professional Geographer 37 (1): 75–81.\n\n\nGiraud, Timothée. 2021. “Linemap: Line Maps.” https://CRAN.R-project.org/package=linemap.\n\n\n———. 2022a. “Mapsf: Thematic Cartography.” https://CRAN.R-project.org/package=mapsf.\n\n\n———. 2022b. “Tanaka: Design Shaded Contour Lines (or Tanaka) Maps.” https://CRAN.R-project.org/package=tanaka.\n\n\nJeworutzki, Sebastian. 2020. “Cartogram: Create Cartograms with r.” https://CRAN.R-project.org/package=cartogram.\n\n\nLambert, Nicolas. 2015. “Les Anamorphoses Cartographiques.” Blog. Carnet Néocartographique. https://neocarto.hypotheses.org/366.\n\n\nOlson, Judy M. 1976. “Noncontiguous Area Cartograms.” The Professional Geographer 28 (4): 371–80.\n\n\nPaull, John, and Benjamin Hennig. 2016. “Atlas of Organics: Four Maps of the World of Organic Agriculture.” Journal of Organics 3 (1): 25–32.\n\n\nTanaka, Kitiro. 1950. “The Relief Contour Method of Representing Topography on Maps.” Geographical Review 40 (3): 444. https://doi.org/10.2307/211219."
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"text": "RGeoHealth (Herbreteau, Révillion, and Trimaille 2018)\n\n# remotes::install_git(\"https://framagit.org/espace-dev/geohealth/RGeoHealth\")\n# library(geohealth)\n\n\n\n\n\nHerbreteau, Vincent, Christophe Révillion, and Etienne Trimaille. 2018. “GeoHealth and QuickOSM, two QGIS plugins for health applications.” In Earth Systems - Environmental Sciences : QGIS in Remote Sensing Set, edited by Nicolas Baghdadi, Clément Mallet, and Mehrez Zribi, 1:257–86. QGIS and Generic Tools. ISTE. https://hal.archives-ouvertes.fr/hal-01787435."
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"text": "This section aims at providing some basic statistical tools to study the spatial distribution of the cases." },
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"section": "7.1 Import and visualize epidemiological data",
"text": "7.1 Import and visualize epidemiological data\nIn this section, we load data that reference the cases of an imaginary disease throughout Cambodia.\n\nlibrary(sf)\n\n#Import Cambodia country border\ncountry = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"country\", quiet = TRUE)\n#Import provincial administrative border of Cambodia\neducation = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"education\", quiet = TRUE)\n#Import district administrative border of Cambodia\ndistrict = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"district\", quiet = TRUE)\n\n# Import locations of cases from an imaginary disease\ncases = st_read(\"data_cambodia/cambodia.gpkg\", layer = \"cases\", quiet = TRUE)\ncases = subset(cases, Disease == \"W fever\")\n\n# Aggregate cases over districts\ndistrict$cases <- lengths(st_intersects(district, cases))\n\nThe first step of any statistical analysis always consists on visualizing the data to check they were correctly loaded and to observe general pattern of the cases.\n\n# View the cases object\nhead(cases)\n\nSimple feature collection with 6 features and 2 fields\nGeometry type: MULTIPOINT\nDimension: XY\nBounding box: xmin: 255891 ymin: 1179092 xmax: 506647.4 ymax: 1467441\nProjected CRS: WGS 84 / UTM zone 48N\n id Disease geom\n1 0 W fever MULTIPOINT ((280036.2 12841...\n2 1 W fever MULTIPOINT ((451859.5 11790...\n3 2 W fever MULTIPOINT ((255891 1467441))\n4 5 W fever MULTIPOINT ((506647.4 12322...\n5 6 W fever MULTIPOINT ((440668 1197958))\n6 7 W fever MULTIPOINT ((481594.5 12714...\n\n# Map the cases\nlibrary(mapsf)\n\nmf_map(x = district, border = \"white\")\nmf_map(x = country,lwd = 2, col = NA, add = TRUE)\nmf_map(x = cases, lwd = .5, col = \"#990000\", pch = 20, add = TRUE)"
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"text": "7.2 Basics statistics\nThe problem is usually expressed by defining two hypothesis : the null hypothesis (H0), i.e. an a priori hypothesis of the studied phenomenon (e.g. the situation is a random) and the alternative hypothesis (HA), e.g. the situation is not random. The main principle is to measure how likely the observed situation belong to the ensemble of situation that are possible under the H0 hypothesis.\nThe statistical analysis performed relies on the type of data.\n\n7.2.1 Spatial autocorrelation (Moran’s I test)\nA popular test for spatial autocorrelation is the Moran’s test.\nMoran’s I test tells us whether nearby units tend to exhibit similar rates. It ranges from -1 to +1, whith a value of -1 denoting that units with low rates are located near other units with high rates, while a Moran’s I value of +1 indicates a concentration of spatial units exhibiting similar rates.\nWe will compute the Moran’s statistics using spdep and Dcluster packages. This package provides a collection of functions to analyze spatial correlations of polygons and works with sp objects. Dcluster package provides a set of functions for the detection of spatial clusters of disease using count data.\n\n# Compte incidence in each district (per 100 000 population)\ndistrict$incidence <- district$cases/district$T_POP * 100000\n\n# Plot the incidence histogramm\nhist(log(district$incidence))"
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"text": "7.3 Cluster analysis\nIn epidemiology, the definition of a cluster\n\n7.3.1 Population-based clusters (kulldorf statistic)\nKulldorff ’s spatial scan statistic identifies the most likely disease clusters maximizing the likelihood that disease cases are located within a set of concentric circles that are moved across the study area.\n\n\n7.3.2 Expectation-based cluster\nIn many case, population is not specific enough to\n\n\n7.3.3 To go further …"
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"text": "Agafonkin, Vladimir. 2015. “Leaflet Javascript Libary.”\n\n\nAppelhans, Tim, Florian Detsch, Christoph Reudenbach, and Stefan\nWoellauer. 2022. “Mapview: Interactive Viewing of Spatial Data in\nr.” https://CRAN.R-project.org/package=mapview.\n\n\nAppelhans, Tim, Kenton Russell, and Lorenzo Busetto. 2020.\n“Mapedit: Interactive Editing of Spatial Data in r.” https://CRAN.R-project.org/package=mapedit.\n\n\nBivand, Roger, Tim Keitt, and Barry Rowlingson. 2022. “Rgdal:\nBindings for the ’Geospatial’ Data Abstraction Library.” https://CRAN.R-project.org/package=rgdal.\n\n\nBivand, Roger, and Colin Rundel. 2021. “Rgeos: Interface to\nGeometry Engine - Open Source (’GEOS’).” https://CRAN.R-project.org/package=rgeos.\n\n\nBrunet, Roger, Robert Ferras, and Hervé Théry. 1993. Les Mots de La\ngéographie: Dictionnaire Critique. 03) 911 BRU.\n\n\nCambon, Jesse, Diego Hernangómez, Christopher Belanger, and Daniel\nPossenriede. 2021. “Tidygeocoder: An r Package for\nGeocoding” 6: 3544. https://doi.org/10.21105/joss.03544.\n\n\nCauvin, Colette, Francisco Escobar, and Aziz Serradj. 2013. Thematic\nCartography, Cartography and the Impact of the Quantitative\nRevolution. Vol. 2. John Wiley & Sons.\n\n\nCheng, Joe, Bhaskar Karambelkar, and Yihui Xie. 2022. “Leaflet:\nCreate Interactive Web Maps with the JavaScript ’Leaflet’\nLibrary.” https://CRAN.R-project.org/package=leaflet.\n\n\nDorling, Daniel. 1996. Area Cartograms: Their Use and Creation,\nConcepts and Techniques in Modern Geography. Vol. 59. CATMOG:\nConcepts and Techniques in Modern Geography. Institute of British\nGeographers.\n\n\nDougenik, James A, Nicholas R Chrisman, and Duane R Niemeyer. 1985.\n“An Algorithm to Construct Continuous Area Cartograms.”\nThe Professional Geographer 37 (1): 75–81.\n\n\nDunnington, Dewey. 2021. “Ggspatial: Spatial Data Framework for\nGgplot2.” https://CRAN.R-project.org/package=ggspatial.\n\n\nGDAL/OGR contributors. n.d. GDAL/OGR Geospatial Data\nAbstraction Software Library. Open Source Geospatial Foundation. https://gdal.org.\n\n\nGilardi, Andrea, and Robin Lovelace. 2021. “Osmextract: Download\nand Import Open Street Map Data Extracts.” https://CRAN.R-project.org/package=osmextract.\n\n\nGiraud, Timothée. 2021a. “Linemap: Line Maps.” https://CRAN.R-project.org/package=linemap.\n\n\n———. 2021b. “Maptiles: Download and Display Map Tiles.” https://CRAN.R-project.org/package=maptiles.\n\n\n———. 2022a. “Mapsf: Thematic Cartography.” https://CRAN.R-project.org/package=mapsf.\n\n\n———. 2022b. “Tanaka: Design Shaded Contour Lines (or Tanaka)\nMaps.” https://CRAN.R-project.org/package=tanaka.\n\n\nGiraud, Timothée, and Nicolas Lambert. 2016. “Cartography: Create\nand Integrate Maps in Your r Workflow” 1. https://doi.org/10.21105/joss.00054.\n\n\nGombin, Joel, and Paul-Antoine Chevalier. 2022. “banR: R Client\nfor the BAN API.”\n\n\nHerbreteau, Vincent, Christophe Révillion, and Etienne Trimaille. 2018.\n“GeoHealth and QuickOSM, two QGIS plugins for\nhealth applications.” In Earth\nSystems - Environmental Sciences : QGIS in Remote Sensing\nSet, edited by Nicolas Baghdadi, Clément Mallet, and Mehrez\nZribi, 1:257–86. QGIS and Generic Tools. ISTE. https://hal.archives-ouvertes.fr/hal-01787435.\n\n\nHijmans, Robert J. 2022a. “Raster: Geographic Data Analysis and\nModeling.” https://CRAN.R-project.org/package=raster.\n\n\n———. 2022b. “Terra: Spatial Data Analysis.” https://CRAN.R-project.org/package=terra.\n\n\nJeworutzki, Sebastian. 2020. “Cartogram: Create Cartograms with\nr.” https://CRAN.R-project.org/package=cartogram.\n\n\nLambert, Nicolas. 2015. “Les Anamorphoses Cartographiques.”\nBlog. Carnet Néocartographique. https://neocarto.hypotheses.org/366.\n\n\nLi, Xingong. 2009. “Map Algebra and Beyond : 1. Map Algebra for\nScalar Fields.” https://slideplayer.com/slide/5822638/.\n\n\nMadelin, Malika. 2021. “Analyse d’images Raster (Et\nTélédétection).” https://mmadelin.github.io/sigr2021/SIGR2021_raster_MM.html.\n\n\nMennis, Jeremy. 2015. “Fundamentals of GIS : Raster\nOperations.” https://cupdf.com/document/gus-0262-fundamentals-of-gis-lecture-presentation-7-raster-operations-jeremy.html.\n\n\nNowosad, Jakub. 2021. “Image Processing and All Things\nRaster.” https://nowosad.github.io/SIGR2021/workshop2/workshop2.html.\n\n\nOlson, Judy M. 1976. “Noncontiguous Area Cartograms.”\nThe Professional Geographer 28 (4): 371–80.\n\n\nPadgham, Mark, Bob Rudis, Robin Lovelace, and Maëlle Salmon. 2017.\n“Osmdata” 2. https://doi.org/10.21105/joss.00305.\n\n\nPaull, John, and Benjamin Hennig. 2016. “Atlas of Organics: Four\nMaps of the World of Organic Agriculture.” Journal of\nOrganics 3 (1): 25–32.\n\n\nPebesma, Edzer. 2018b. “Simple Features for r:\nStandardized Support for Spatial Vector Data” 10. https://doi.org/10.32614/RJ-2018-009.\n\n\n———. 2018a. “Simple Features for R: Standardized Support for\nSpatial Vector Data.” The R Journal 10 (1): 439. https://doi.org/10.32614/rj-2018-009.\n\n\n———. 2021. “Stars: Spatiotemporal Arrays, Raster and Vector Data\nCubes.” https://CRAN.R-project.org/package=stars.\n\n\nPebesma, Edzer J., and Roger S. Bivand. 2005. “Classes and Methods\nfor Spatial Data in r” 5. https://CRAN.R-project.org/doc/Rnews/.\n\n\nPROJ contributors. 2021. PROJ Coordinate Transformation\nSoftware Library. Open Source Geospatial Foundation. https://proj.org/.\n\n\nRacine, Etienne B. 2016. “The Visual Raster Cheat Sheet.”\nhttps://rpubs.com/etiennebr/visualraster.\n\n\nTanaka, Kitiro. 1950. “The Relief Contour Method of Representing\nTopography on Maps.” Geographical Review 40 (3): 444. https://doi.org/10.2307/211219.\n\n\nTennekes, Martijn. 2018. “Tmap: Thematic\nMaps in r” 84. https://doi.org/10.18637/jss.v084.i06.\n\n\nTomlin, C. Dana. 1990. Geographic Information Systems and\nCartographic Modeling. Prentice Hall.\n\n\nWickham, Hadley. 2016. “Ggplot2: Elegant Graphics for Data\nAnalysis.” https://ggplot2.tidyverse.org."
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