Scripts and data for L05

R/BIA_L05_Geographic_Time_Data.Rmd

+---
+title: "Practical tasks working with geographic and time-related data (EESYS-BIA-M, L05)"
+output: html_notebook
+editor_options: 
+  chunk_output_type: inline
+---
+
+# Practicals for geographic data
+
+Germany publishes geographic data as ESRI shapefiles on the Website of the [Bundesamt für Kartographie](http://www.geodatenzentrum.de/geodaten/gdz_rahmen.gdz_div?gdz_spr=deu&gdz_akt_zeile=5&gdz_anz_zeile=1&gdz_user_id=0)
+
+For illusrration, the political borders (Verwaltungsgrenzen) are used on a scale of 1:250 000 (VG250)
+
+There are two datasets available:
+  * VG250 Kompakt: contains the lines and general data (>120 MB)
+  * VG250 Ebenen: contains the detailed data (>70MB)
+  
+Selected layers are available in the `/data/` folder of this repository.
+
+```{r load German political borders}
+library(sp)
+library(rgdal)
+
+#read the shapefile
+ger.shp <- readOGR("../data/DE_Admin_Geodata/VG250_Kompakt/", "VG250_L") #lines
+
+#the attribute AGZ contains the type of the boundary (AGZ = 'Art der Grenze')
+table(ger.shp$AGZ)
+  
+#Plot lines
+plot(ger.shp[ger.shp$AGZ==1, ])         #German main border
+lines(ger.shp[ger.shp$AGZ==2, ], col=2) #German state borders
+lines(ger.shp[ger.shp$AGZ==3, ], col=3) #German Regierungsbezirk
+lines(ger.shp[ger.shp$AGZ==4, ], col=4) #Kreise
+
+```
+
+```{r load German states as polygons}
+#read teh shapefile
+ger.shp <- readOGR("../data/DE_Admin_Geodata/VG250_Ebenen/", "VG250_LAN")
+
+#basic plot of the data  - looks similar to previous plot
+plot(ger.shp)
+
+#Here, we have polygons that we can colorate
+plot(ger.shp, col=1:23)
+
+  ger.shp@data
+  ?plot.sp
+  proj4string(ger.shp)
+  head(ger.shp@)
+  plot(ger.shp, axit="t")
+
+```
+
+As an example, the housing statistics from the German Federal Ministry of Statistics is used
+Wohngebäude, Wohnungen, Wohnfläche: Bundesländer, Stichtag, Anzahl der Wohnungen
+
+By this example, the `readr` package is illustrated
+
+```{r visualize some data}
+library(readr)
+housing_statistic_DE <- read_csv2("../data/housing_statistic_germany_31231-0003.csv", 
+    col_types = cols(
+      date = col_number(),
+      state = col_character(),
+      `1_app_buildings` = col_integer(),
+      `1_app_dwellings` = col_integer(),
+      `1_app_livingareas` = col_integer(),
+      `2_app_buildings` = col_integer(),
+      `2_app_dwellings` = col_integer(),
+      `2_app_livingareas` = col_integer(),
+      `3_more_app_buildings` = col_integer(),
+      `3_more_app_dwellings` = col_integer(),
+      `3_more_app_livingarea` = col_integer(),
+      dormitory_buildings = col_integer(),
+      dormitory_dwellings = col_integer(),
+      dormitory_livingarea = col_integer(),
+      total_buildings = col_integer(),
+      total_dwellings = col_integer(),
+      total_livingarea = col_integer()
+    ),
+  col_names = c("date", "state", 
+    "1_app_buildings", "1_app_dwellings", "1_app_livingareas", 
+    "2_app_buildings", "2_app_dwellings", "2_app_livingareas", 
+    "3_more_app_buildings", "3_more_app_dwellings",  "3_more_app_livingarea", 
+    "dormitory_buildings", "dormitory_dwellings", "dormitory_livingarea", 
+    "total_buildings", "total_dwellings", "total_livingarea"),
+  skip = 8, n_max = 16, locale = locale(date_names = "de", 
+                                        encoding = "latin1", 
+                                        decimal_mark = ",", 
+                                        grouping_mark = "."))  
+
+
+#calculate the relative distribution of total living area 
+housing_statistic_DE$total_livingarea_rel <- housing_statistic_DE$total_livingarea / max(housing_statistic_DE$total_livingarea)
+
+#the HSV function definex colors by means of hue, saturation and darkness value
+color_grades <- hsv(h=0.6, s=housing_statistic_DE$total_livingarea_rel, v = 1)
+pos_states_map_statistics <- match(ger.shp$GEN, housing_statistic_DE$state)
+
+plot(ger.shp, col=color_grades[pos_states_map_statistics], 
+     main="Total living space area in the German states")
+axis(1)
+axis(2)
+```
+
+
+```{r plot coordinaes on the map}
+
+# the WGS84 coordinates of Bamberg are
+bamberg <- SpatialPoints(list(x=10.891667, y=49.891667))
+
+# transform the german UTM coordinates to WGS84 coordinates
+ger.shp.wgs84 <- spTransform(ger.shp, CRS("+proj=longlat +datum=WGS84"))
+plot(ger.shp.wgs84)
+axis(1)
+axis(2)
+points(bamberg, col=2)
+```
+

data/DE_Admin_Geodata/VG250_Ebenen/VG250_LAN.cpg

+UTF-8
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data/DE_Admin_Geodata/VG250_Ebenen/VG250_LAN.prj

+PROJCS["ETRS_1989_UTM_Zone_32N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",9.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]
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data/DE_Admin_Geodata/VG250_Ebenen/VG250_STA.cpg

+UTF-8
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data/DE_Admin_Geodata/VG250_Ebenen/VG250_STA.prj

+PROJCS["ETRS_1989_UTM_Zone_32N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",9.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]
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data/DE_Admin_Geodata/VG250_Kompakt/VG250_L.cpg

+UTF-8
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data/DE_Admin_Geodata/VG250_Kompakt/VG250_L.prj

+PROJCS["ETRS_1989_UTM_Zone_32N",GEOGCS["GCS_ETRS_1989",DATUM["D_ETRS_1989",SPHEROID["GRS_1980",6378137.0,298.257222101]],PRIMEM["Greenwich",0.0],UNIT["Degree",0.0174532925199433]],PROJECTION["Transverse_Mercator"],PARAMETER["False_Easting",500000.0],PARAMETER["False_Northing",0.0],PARAMETER["Central_Meridian",9.0],PARAMETER["Scale_Factor",0.9996],PARAMETER["Latitude_Of_Origin",0.0],UNIT["Meter",1.0]]
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