Title:	The Swiss Municipal Data Merger Tool Maps Municipalities Over Time
Version:	1.2.0
Description:	In Switzerland, the landscape of municipalities is changing rapidly mainly due to mergers. The Swiss Municipal Data Merger Tool automatically detects these mutations and maps municipalities over time, i.e. municipalities of an old state to municipalities of a new state. This functionality is helpful when working with datasets that are based on different spatial references. The package's idea and use case is discussed in the following article: <doi:10.1111/spsr.12487>.
Imports:	dplyr, XML, tibble, curl, rvest, xml2
Suggests:	testthat, roxygen2, knitr, rmarkdown
URL:	https://github.com/ValValetl/SMMT
BugReports:	https://github.com/ValValetl/SMMT/issues
VignetteBuilder:	knitr
License:	GPL-3
Encoding:	UTF-8
RoxygenNote:	7.3.1
NeedsCompilation:	no
Packaged:	2024-05-19 17:29:42 UTC; valentin
Author:	Valentin Knechtl [aut, cre]
Maintainer:	Valentin Knechtl <valentinknechtl@gmail.com>
Repository:	CRAN
Date/Publication:	2024-05-20 10:40:02 UTC

SMMT - The Swiss Municipal Data Merger Tool Maps Municipalities Over Time

Description

In Switzerland, the landscape of municipalities is changing rapidly mainly due to mergers. The Swiss Municipal Data Merger Tool automatically detects these mutations and maps municipalities of an old state to municipalities of a new state. This functionality is helpful when working with datasets that are based on different spatial references. The spatial reference in this context signifies a set of municipalities at a given point in time.

Details

For detailed information and examples, see map_old_to_new_state

Author(s)

Maintainer: Valentin Knechtl valentinknechtl@gmail.com (ORCID)

See Also

Useful links:

• https://github.com/ValValetl/SMMT

• Report bugs at https://github.com/ValValetl/SMMT/issues

Get date of last municipal inventory update

Description

Obtain the most recent change in the municipal inventory database. Most but not all municipal mutations are made at first of january.

Usage

date_of_last_update(mutations)

Arguments

Value

A Date object of length one.

Download municipality inventory

Description

This functions downloads and extracts the municipality inventory form a defined online source.

Usage

download_municipality_inventory(
  url = get_current_url(),
  path = getwd(),
  verbose = TRUE
)

Arguments

Value

Character vector of length one. File path to the extracted XML file.

Filter by date

Description

Filter for existing municipalities at a specific point in time.

Usage

filter_date(tbl, date)

Arguments

Value

A tibble which is a subset of tbl

Get URL of current XML data set

Description

Extract the url from the static source web page.

Usage

get_current_url()

Value

Returns URL to municipality inventory

Get irreversible municipality mutations

Description

This function detects irreversible mutations.

Usage

get_irreversible_municipality_mutations(mutations)

Arguments

Details

Irreversible mutations are defined as mutations during which territories are split up. There are different types of irreversible mutations drawn from the below cited document. In contrast, normal mutations signify a simple merging of territory which accounts for most of the mutations in Switzerland since 1960 whereas irreversible mutations occurred only rarely. The aim of this function is to filter for these irreversible mutations. These can then be treated separatly.

Definitions for different types of territory split ups are based on: Erläuterungen und Anwendungen - Historisierte Gemeindeverzeichnis der Schweiz (2017).

Value

A tibble with all the instances of irreversibe mutations. The irreversibility cause is part of the output.

Examples

  mutations <- structure(list(hist_id = c(11320L, 13668L, 13669L),
 district_hist_id = c(10024L, 10024L, 10024L),
 kanton_abbr = c("AG", "AG", "AG"),
 bfs_nr = c(4061L, 4061L, 4084L), 
 name = c("Arni-Islisberg", "Arni (AG)", "Islisberg"), 
 admission_nr = c(1000L, 1481L, 1481L), 
 admission_mode = c(20L, 21L, 21L), 
 admission_date = structure(c(-3653, 4748,  4748),
 class = c("Date")), 
 abolition_nr = c(1481L, NA, NA), 
 abolition_mode = c(29L, NA, NA), 
 abolition_date = structure(c(4747, NA, NA),
 class = c("Date")), 
 change_date = structure(c(4747, 4748, 4748),
 class = c("Date"))), 
 row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"))
   
irreversible_mutations <- get_irreversible_municipality_mutations(mutations)

Import the Swiss Municipality inventory

Description

This function imports the Swiss municipality inventory from the raw XML resource into R as a tibble. The imported table is the basis to map the Swiss municipalities from an old to a new state (see map_old_to_new_state).

Usage

import_CH_municipality_inventory(file_path)

Arguments

Details

This imported Swiss municipality inventory is a database with the complete mutation history that occured since 01.01.1960. The Swiss municipality inventory is made available by the Federal Statistical Office and updated regularly to keep track of new mutations.

Download

See BfS webpage for infos about Swiss municipality inventory: Historisiertes Gemeindeverzeichnis

Value

A list with two tables in the form of tibble objects.

1. Municipality mutations.

2. Canton mutations

See Also

map_old_to_new_state

Map municipalities of old state to municipalities of new state

Description

This function maps the Swiss municipalities of an old state to municipalities of a new state.

Usage

map_old_to_new_state(mutations, state_old, state_new)

Arguments

Details

Approach

1. Download the Swiss municipality inventory

2. Import it into R workspace with import_CH_municipality_inventory

3. Set the old state and the new state (see example)

4. Get the mapping table with this function

Example Daettwil / Baden

On 1.1.1962 Daettwil (Bfs Nr. 4025) merged with Baden (Bfs Nr. 4021). Let's define

• old_state <- as.Date("1961-01-01")

• new_state <- as.Date("1963-01-01")

• Result:

  bfs_nr_new	name_new	bfs_nr_old	name_old
  4021	Baden	4021	Baden
  4021	Baden	4025	Daettwil

Value

A list with 4 elements:

1. mapped: A tibble with the mapped municipalities

2. unmapped: A tibble with the unmapped municipalities

3. state_old: see above

4. state_new: see above

Examples

mutations <- structure(list(hist_id = c(11227L, 11240L, 13189L),
district_hist_id = c(10025L, 10025L, 10025L),
kanton_abbr = c("AG", "AG", "AG"),
bfs_nr = c(4025L, 4021L, 4021L),
name = c("Daettwil", "Baden", "Baden"),
admission_nr = c(1000L, 1000L, 1004L),
admission_mode = c(20L, 20L, 26L),
admission_date = structure(c(-3653, -3653, -2922),
class = c("Date")),
abolition_nr = c(1004L, 1004L, NA),
abolition_mode = c(29L, 26L, NA),
abolition_date = structure(c(-2923, -2923, NA),
class = c("Date")),
change_date = structure(c(-2923, -2923, -2922), class = c("Date"))),
row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"))

mapping_object <- map_old_to_new_state(mutations,
as.Date("1961-01-01"), as.Date("1963-01-01"))

Most recent changes

Description

Returns a table with the most recently changed municipalities.

Usage

most_recent_changes(mutations)

Arguments

Municipality counter

Description

Count the municipalities for a set of dates. Either at the national or cantonal level. See vignette for details.

Usage

municipality_counter(
  mutations,
  dates,
  geo_level = "ch",
  include_cant_lakes = FALSE
)

Arguments

Value

A tibble with the municipality count per date and specified geography.

Note

All entities that have a bfs nr are counted (e.g. also Gemeindefreie Gebiete). This is not exactly what the BfS does in the webtool Applikation der Schweizer Gemeinden. However, it is not possible to distinguish "Gemeinden" und "Gemeindefreie Gebiete" generically, based on the information in the Gemeindeverzeichnis.

Mutation count

Description

Count number of mutations in a given time period

Usage

mutation_count(
  mutations,
  start_date,
  end_date = Sys.Date(),
  territorial_changes_only = FALSE
)

Arguments

Details

Approach

1. Download the Swiss municipality inventory

2. Import it into R workspace with import_CH_municipality_inventory

3. Set the old state and the new state (see example)

4. Get the mapping table with this function

Example Daettwil / Baden

On 1.1.1962 Daettwil (Bfs Nr. 4025) merged with Baden (Bfs Nr. 4021). Let's define

• old_state <- as.Date("1961-01-01")

• new_state <- as.Date("1963-01-01")

• Result:

  bfs_nr_new	name_new	bfs_nr_old	name_old
  4021	Baden	4021	Baden
  4021	Baden	4025	Daettwil

Value

A list with 4 elements:

1. mapped: A tibble with the mapped municipalities

2. unmapped: A tibble with the unmapped municipalities

3. state_old: see above

4. state_new: see above

Examples

mutations <- structure(list(hist_id = c(11227L, 11240L, 13189L),
district_hist_id = c(10025L, 10025L, 10025L),
kanton_abbr = c("AG", "AG", "AG"),
bfs_nr = c(4025L, 4021L, 4021L),
name = c("Daettwil", "Baden", "Baden"),
admission_nr = c(1000L, 1000L, 1004L),
admission_mode = c(20L, 20L, 26L),
admission_date = structure(c(-3653, -3653, -2922),
class = c("Date")),
abolition_nr = c(1004L, 1004L, NA),
abolition_mode = c(29L, 26L, NA),
abolition_date = structure(c(-2923, -2923, NA),
class = c("Date")),
change_date = structure(c(-2923, -2923, -2922), class = c("Date"))),
row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"))

mapping_object <- map_old_to_new_state(mutations,
as.Date("1961-01-01"), as.Date("1963-01-01"))

Territorial mutation coutn

Description

Count number of mutations in a given time period

Usage

territorial_mutation_count(mutations, start_date, end_date = Sys.Date())

Arguments

Details

Approach

1. Download the Swiss municipality inventory

2. Import it into R workspace with import_CH_municipality_inventory

3. Set the old state and the new state (see example)

4. Get the mapping table with this function

Example Daettwil / Baden

On 1.1.1962 Daettwil (Bfs Nr. 4025) merged with Baden (Bfs Nr. 4021). Let's define

• old_state <- as.Date("1961-01-01")

• new_state <- as.Date("1963-01-01")

• Result:

  bfs_nr_new	name_new	bfs_nr_old	name_old
  4021	Baden	4021	Baden
  4021	Baden	4025	Daettwil

Value

A list with 4 elements:

1. mapped: A tibble with the mapped municipalities

2. unmapped: A tibble with the unmapped municipalities

3. state_old: see above

4. state_new: see above

Examples

mutations <- structure(list(hist_id = c(11227L, 11240L, 13189L),
district_hist_id = c(10025L, 10025L, 10025L),
kanton_abbr = c("AG", "AG", "AG"),
bfs_nr = c(4025L, 4021L, 4021L),
name = c("Daettwil", "Baden", "Baden"),
admission_nr = c(1000L, 1000L, 1004L),
admission_mode = c(20L, 20L, 26L),
admission_date = structure(c(-3653, -3653, -2922),
class = c("Date")),
abolition_nr = c(1004L, 1004L, NA),
abolition_mode = c(29L, 26L, NA),
abolition_date = structure(c(-2923, -2923, NA),
class = c("Date")),
change_date = structure(c(-2923, -2923, -2922), class = c("Date"))),
row.names = c(NA, -3L), class = c("tbl_df", "tbl", "data.frame"))

mapping_object <- map_old_to_new_state(mutations,
as.Date("1961-01-01"), as.Date("1963-01-01"))