https://github.com/jafro96/dasymetric

Dasymetric maps display statistical data in meaningful spatial zones
https://github.com/jafro96/dasymetric

dasymetric-mapping opendata r r-package thematic-maps

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Dasymetric maps display statistical data in meaningful spatial zones

• Host: GitHub
• URL: https://github.com/jafro96/dasymetric
• Owner: JaFro96
• License: other
• Created: 2021-08-12T09:07:58.000Z (over 3 years ago)
• Default Branch: main
• Last Pushed: 2021-09-05T10:50:47.000Z (about 3 years ago)
• Last Synced: 2023-10-20T20:32:57.592Z (about 1 year ago)
• Topics: dasymetric-mapping, opendata, r, r-package, thematic-maps
• Language: R
• Homepage:
• Size: 16.6 MB
• Stars: 0
• Watchers: 3
• Forks: 0
• Open Issues: 0
• Metadata Files:
  • Readme: README.Rmd
  • License: LICENSE

Awesome Lists containing this project

README

        
---

output: github_document

---

```{r chunkoptions, include = FALSE}

knitr::opts_chunk$set(

  collapse = TRUE,

  comment = "#>",

  fig.path = "man/figures/README-",

  out.width = "100%"

)

```

# dasymetric

The goal of [dasymetric mapping](https://en.wikipedia.org/wiki/Dasymetric_map) is to display statistical data (like census data) in meaningful spatial zones. 

## Installation

And the development version from [GitHub](https://github.com/) with:

``` r

# install.packages("devtools")

devtools::install_github("JaFro96/dasymetric")

```

## Example

As a case study we try to predict population counts for each district of Münster (Westfalen) using land cover data as ancillary information.

```{r districts, message=FALSE}

# plot population of 2018

require(sf)

load("data/population_counts.rda")

plot(population_counts["population"],breaks = c(0,5000,10000,15000,20000,25000,30000,35000,40000,45000), main="Population (2018)")

```

Below the dasymetric map is plotted which exhibits similar patterns in the population distribution

```{r dasymetric, warning=FALSE, message=FALSE}

require(dasymetric)

load("data/corine_18.rda")

urban = prep_landuse(corine_18)

# source geometry covering entire Münster 

source_geom = st_union(population_counts)

# add population of Münster

source = st_sf(ID = 1, pop_sum = sum(population_counts["population"]$population), source_geom)

# dasymetric map with landuse information as ancillary data

dm_pop = dasymetric_map(population_counts, source, urban, extensive = "pop_sum")

plot(dm_pop["pop_sum"],breaks = c(0,5000,10000,15000,20000,25000,30000,35000,40000,45000),main="Dasymetric Population Map based on Land Use Information (2018)")

```

... contrary to the population distribution using area-weighted interpolation:

```{r aw-interpolation,  message = FALSE}

require(areal)

# Area-weighted interpolation of Münsters districts

aw_pop = aw_interpolate(population_counts,NR_STATIST,source = source, sid = ID,weight = "sum", extensive = "pop_sum", output = "sf")

plot(aw_pop["pop_sum"],breaks = c(0,5000,10000,15000,20000,25000,30000,35000,40000,45000),main="Area-weighted Interpolation of Population (2018)")

```

## Data Sources

- CORINE Land Cover 5 ha --> [© GeoBasis-DE / BKG (2021)](https://gdz.bkg.bund.de/index.php/default/catalog/product/view/id/1071/s/corine-land-cover-5-ha-stand-2018-clc5-2018/category/8/?___store=default)

- district boundaries --> [opendata.stadt-muenster.de](https://opendata.stadt-muenster.de/dataset/geokoordinaten-der-stadtteil-grenzen-geometriedaten-der-kleinr%C3%A4umigen-gebietsgliederung)

- population counts --> [opendata.stadt-muenster.de](opendata.stadt-muenster.de)