Author: Krzysztof Dyba, Jakub Nowosad

# Topographic Database

#### Krzysztof Dyba, Jakub Nowosad

## Definition

Topographic Database (pl. Baza Danych Obiektów Topograficznych) is a vector (object) database containing the spatial location of topographic objects with their characteristics for Poland. The content and detail of the database correspond to the topographic map in the scale 1:10000. The thematic scope includes information on water network, communication network, land cover, buildings and technical structures, utility infrastructure, land use, protected areas, territorial division units, and other objects. The database is available in the Geography Markup Language (GML) format. The source of its data comes from:

- other spatial databases maintained by Polish Head Office of Geodesy and Cartography (e.g. Register of Towns, Streets and Addresses, Register of Land and Buildings, State Register of Borders),
- databases maintained by other ministries or institutions (e.g., Ministry of Infrastructure, State Water Management, General Directorate for Environmental Protection),
- fieldworks.

## Purpose

The purpose of this vignette is to perform spatial operations on vector data from the Topographic Database. We focus on four cases, taking into account different types of geometry, i.e. point, line, and polygon, and their attributes. Also, we show how they can be visualized.

## Analysis

    # attach packages
    library(sf)
    library(rgugik)

Our analysis area is the bieszczadzki county located in the Subcarpathian (podkarpackie) voivodeship. It is the farthest south area in Poland that also has the lowest population density (19 people on km2).

### Database

We start by downloading the topographic database for our county using the `topodb_download()` function.

    # 22.4 MB
    topodb_download("bieszczadzki", outdir = "./data")

If you run into any problem with the download, remember that you can pass another download method from `download.file()` as a function argument.

    topodb_download(req_df, outdir = "./data", method = "wget")

The downloaded database consists of many files in the GML format. All the data necessary for analyzes can be found in the `data/PL.PZGiK.332.1801/BDOT10k/` location. A brief description of the structure of this database can be found here.

First, let’s load the file with administrative units (“PL.PZGiK.332.1801__OT_ADJA_A.xml”) using the sf package and its `read_sf()` function.

    territory = read_sf("data/PL.PZGiK.332.1801/BDOT10k/PL.PZGiK.332.1801__OT_ADJA_A.xml")

The file contains administrative units at various levels, let’s choose the lowest level, i.e. communes. There are three types of communes in this dataset, specified in the `rodzaj` column: urban (GM), rural (GW), and urban-rural (Gmw). Let’s select them.

    communes = territory[territory$rodzaj %in% c("GM", "GW", "Gmw"), "rodzaj"]
    table(communes$rodzaj)

    ##
    ## Gmw  GW
    ##   1   2

We can see that the bieszczadzki county consists of two rural communes and one urban-rural commune. Let’s visualize it.

    plot(communes, axes = TRUE, main = "Bieszczadzki county")

### Lengths and categories of roads

In the first task, we calculate the lengths of roads, taking into account their categories. Road data can be found in the “PL.PZGiK.332.1801__OT_SKDR_L.xml” file.

    roads = read_sf("data/PL.PZGiK.332.1801/BDOT10k/PL.PZGiK.332.1801__OT_SKDR_L.xml")

Let’s plot them. We use the `plot()` function again, but this time we combine the two layers into one image. The first layer (in the background) contains roads and to add another layer, we have to set the argument `reset` to `"FALSE"`. Then we can add a second layer with the territory borders by setting the `add` argument to `"TRUE"`.

    plot(roads["katZarzadzania"], main = "Road category", reset = FALSE)
    plot(st_geometry(territory), add = TRUE)

We have six road categories related to the managing entity. Those are: national (K), voivodeship (W), county (P), communal (G), institutional (Z), and other (I) roads.

We use the `st_length()` function to find the length of each object in the table. Next, we create a data frame consisting of the road category and its length. Then we aggregate this data frame and calculate the sum of the lengths for each category.

    length_roads = st_length(roads)
    length_roads = data.frame(length = length_roads,
                              class = as.factor(roads$katZarzadzania))
    length_roads = aggregate(length ~ class, data = length_roads, FUN = sum)

The results are given in meters - let’s convert them into kilometers.

    # convert to [km]
    length_roads$length = units::set_units(length_roads$length, "km")

Let’s also change the names of the categories.

    road_class = c("communal", "other", "national", "county", "voivodeship",
                   "institutional")
    levels(length_roads$class) = road_class

Now we can see the results. The `other` type of roads dominates that consists mainly of non-public roads.

    length_roads

    ##           class          length
    ## 1      communal  186.17329 [km]
    ## 2         other 2819.55806 [km]
    ## 3      national   19.24996 [km]
    ## 4        county  187.17870 [km]
    ## 5   voivodeship  105.67567 [km]
    ## 6 institutional   10.44893 [km]

We can also calculate the total length of the roads in this area.

    sum(length_roads$length)

    ## 3328.285 [km]

The result is about 3328.285 km. Another aspect of the data that we can investigate is the density of the road network. We need to calculate the total area first, and then divide the total road length by the total area.

    communes_area = sum(st_area(communes))
    communes_area = units::set_units(communes_area, "km2")
    density = sum(length_roads$length)/communes_area
    density = units::set_units(density, "km/km2")
    density

    ## 2.921031 [km/km2]

The road density is about 2.92 km/km2.

### Roads through the rivers

Another dataset included in the Topographic Database contains rivers for this area (“PL.PZGiK.332.1801__OT_SWRS_L.xml”).

    rivers = read_sf("data/PL.PZGiK.332.1801/BDOT10k/PL.PZGiK.332.1801__OT_SWRS_L.xml")
    rivers = rivers[rivers$rodzaj == "Rz", ] # select only rivers

Rivers are divided into smaller sections with different parameters, such as river width or data source. Let’s merge sections from the same rivers into a single feature (geometry) - we can use the attribute with an ID (`idMPHP`) for that purpose. We can also give each river a category number by creating a sequence from 1 to n using the `seq_len` function.

    rivers = aggregate(rivers[, c("geometry", "idMPHP")],
                       list(rivers$idMPHP),
                       sum)
    rivers$idMPHP = seq_len(length(unique(rivers$idMPHP)))
    rivers$idMPHP = as.factor(rivers$idMPHP)

Let’s visualize the rivers’ courses.

    plot(rivers["idMPHP"], main = "Rivers", reset = FALSE)
    plot(st_geometry(territory), add = TRUE)

With rivers and roads, we can designate points of intersection that symbolize bridges and crossings. We can use the `st_intersection()` function for this.

    bridges = st_geometry(st_intersection(rivers, roads))
    length(bridges)

    ## [1] 81

We get 81 such points. Let’s plot them.

    # use 'dev.off()' to reset previous plot
    plot(st_geometry(rivers), main = "Bridges and crossings", col = "blue")
    plot(st_geometry(territory), add = TRUE)
    plot(bridges, add = TRUE, pch = 20)

### Land cover

Land cover is the physical material at the surface of the earth like grass, trees, bare ground, water, etc. Let’s check the land cover data for our county - it is stored in files with the `PT` prefix. We use the `list.files()` function to list them. The `pattern` argument is important here because it determines what files should be selected. Our `pattern` should look like this: `PT+.+A\\.xml$` - only files containing areal data (A) of land cover (PT) will be listed.

    files = list.files("data/PL.PZGiK.332.1801/BDOT10k",
                       pattern = "PT+.+A\\.xml$",
                       full.names = TRUE)

    # print filenames
    basename(files)

    ##  [1] "PL.PZGiK.332.1801__OT_PTGN_A.xml"
    ##  [2] "PL.PZGiK.332.1801__OT_PTKM_A.xml"
    ##  [3] "PL.PZGiK.332.1801__OT_PTLZ_A.xml"
    ##  [4] "PL.PZGiK.332.1801__OT_PTNZ_A.xml"
    ##  [5] "PL.PZGiK.332.1801__OT_PTPL_A.xml"
    ##  [6] "PL.PZGiK.332.1801__OT_PTRK_A.xml"
    ##  [7] "PL.PZGiK.332.1801__OT_PTSO_A.xml"
    ##  [8] "PL.PZGiK.332.1801__OT_PTTR_A.xml"
    ##  [9] "PL.PZGiK.332.1801__OT_PTUT_A.xml"
    ## [10] "PL.PZGiK.332.1801__OT_PTWP_A.xml"
    ## [11] "PL.PZGiK.332.1801__OT_PTWZ_A.xml"
    ## [12] "PL.PZGiK.332.1801__OT_PTZB_A.xml"

We also can exclude “PL.PZGiK.332.1801__OT_PTSO_A.xml” file from the list, because this file contains zero objects (features).

    # drop "OT_PTSO_A.xml"
    files = files[-7]

Let’s prepare the names of the objects to which the data will be loaded. The following names are the extended names of the abbreviations stored in the filenames.

    layer_names = c("fallowlands", "communication", "forest", "undeveloped",
                    "squares", "shrublands", "crops", "grassland",
                    "water", "heaps", "buildings")

Now we load each GML file, naming it from the list above. Instead of using a loop, we can use the `lapply()` function, which performs a specific action for each element of the vector. The action in our case is to load the GML files using `read_sf()`.

    layers = lapply(files, read_sf)
    names(layers) = layer_names

Previously, we used `st_length()` to calculate the line length, now we use corresponding `st_area()` function to calculate the area. Here we use the `lapply()` function analogously, which will work for each item on the list. A similar function is `sapply()`, which returns a vector instead of a list.

    # calculate areas in each layer
    area_landcover = lapply(layers, st_area)
    # sum areas for each layer
    area_landcover = sapply(area_landcover, sum)
    # convert units
    area_landcover = units::set_units(area_landcover, "m^2")
    area_landcover = units::set_units(area_landcover, "km^2")
    names(area_landcover) = layer_names

Let’s see the results (in kilometers).

    area_landcover

    ## Units: [km^2]
    ##   fallowlands communication        forest   undeveloped       squares
    ##    0.07561383    1.97014481  860.17248134    0.27817715    0.29038385
    ##    shrublands         crops     grassland         water         heaps
    ##    1.57266555  255.13528884    0.63447491    8.81181550    0.09220115
    ##     buildings
    ##   10.38781092

Let’s make sure that the total land cover is equal to the area of our county. Some tiny precision differences are possible, so we should set the difference tolerance. This is possible using the `all.equal()` function.

    all.equal(sum(area_landcover), communes_area, tolerance = 0.001)

    ## [1] TRUE

Everything is correct. Let’s present the results as percentages of the area and sort them in descending order.

    landcover_percentage = area_landcover / sum(area_landcover) * 100
    units(landcover_percentage) = NULL # drop units
    landcover_percentage = sort(landcover_percentage, decreasing = TRUE)
    landcover_percentage = round(landcover_percentage, 2)
    landcover_percentage

    ##        forest         crops     buildings         water communication
    ##         75.49         22.39          0.91          0.77          0.17
    ##    shrublands     grassland       squares   undeveloped         heaps
    ##          0.14          0.06          0.03          0.02          0.01
    ##   fallowlands
    ##          0.01

Over 75% of the county’s area is covered by forests and only less than 1% by buildings.

### Buffer

In the last analysis in this vignette, we want to check how many buildings have bus stops within a given distance. We can apply spatial buffers to solve this question. Information about bus stops is in the “PL.PZGiK.332.1801__OT_OIKM_P.xml” file, where they are represented by the OIKM04 value of the `x_kod` attribute.

    bus_stop = read_sf("data/PL.PZGiK.332.1801/BDOT10k/PL.PZGiK.332.1801__OT_OIKM_P.xml")
    bus_stop = bus_stop[bus_stop$x_kod == "OIKM04", ]

Let’s prepare a visualization in which the bus stops are marked with blue dots and the buildings are presented as red polygons.

    buildings = layers$buildings
    plot(st_geometry(communes), main = "Bus stops")
    plot(st_geometry(layers$buildings), add = TRUE, border = "red")
    plot(st_geometry(bus_stop), add = TRUE, pch = 20, cex = 0.7, col = "blue")

Let’s create a buffer for each bus stop with a range of 1 km using `st_buffer()`.

    bus_buffer = st_buffer(bus_stop, 1000)

Now, we can plot it all.

    plot(st_geometry(communes), main = "Bus stops buffers")
    plot(st_geometry(buildings), add = TRUE, border = "red")
    plot(st_geometry(bus_buffer), add = TRUE)

To return the buildings within the buffer range, we can perform the `st_within()` operation.

    buildings_buffer = st_within(buildings, bus_buffer)

The result is a nested list that consists of 2828 buildings and their associated buffers. Let’s count how many buildings are not in any buffer by using `sapply()` as in the previous examples.

    buildings_ex = sapply(buildings_buffer, length)
    buildings_ex = sum(buildings_ex == 0)
    buildings_ex = round(buildings_ex / nrow(buildings) * 100)
    buildings_ex

    ## [1] 14

Answer to our last question: 14% of the buildings in this county do not have access to a bus stop within a 1 km radius.