Generate metadata from Candian census vectors

Description

Add Population, Dwellings, and Household counts to metadata

Usage

add_census_ca_base_variables(meta)

Arguments

Value

tibble with metadata

Aggregate variables in grouped data

Description

Aggregate census data up, assumes data is grouped for aggregation Uses data from meta to determine how to aggregate up

Usage

aggregate_data_with_meta(data, meta, geo = FALSE, na.rm = TRUE, quiet = FALSE)

Arguments

Value

data frame with variables aggregated to new common geography

Examples

# Aggregate population from DA level to grouped by CT_UID
## Not run: 
geo <- cancensus::get_census("CA06",regions=list(CSD="5915022"),level='DA')
meta <- meta_for_additive_variables("CA06","Population")
result <- aggregate_data_with_meta(geo %>% group_by(CT_UID),meta)

## End(Not run)

Check geographic integrety

Description

Sanity check for areas of estimated tongfen correspondence. This is useful if for example the total extent of geo1 and geo2 differ and there are regions at the edges with large difference in overlap.

Usage

check_tongfen_areas(data, correspondence)

Arguments

Value

A table with columns 'TongfenID', geo_identifiers, the areas of the aggregated regions corresponding to each geographic identifier column, the tongfen estimation method and the maximum log ratio of the areas.

Examples

# Estimate a common geography for 2006 and 2016 dissemination areas in the City of Vancouver
# based on the geographic data and check estimation errors
## Not run: 
regions <- list(CSD="5915022")

data_06 <- cancensus::get_census("CA06",regions=regions,geo_format='sf',level="DA") %>%
  rename(GeoUID_06=GeoUID)
data_16 <- cancensus::get_census("CA16",regions=regions,geo_format="sf",level="DA") %>%
  rename(GeoUID_16=GeoUID)

correspondence <- estimate_tongfen_correspondence(list(data_06, data_16),
                                                  c("GeoUID_06","GeoUID_16"))

area_check <- check_tongfen_areas(list(data_06, data_16),correspondence)

## End(Not run)

Check geographic integrety

Description

Sanity check for areas of estimated tongfen correspondence. This is useful if for example the total extent of geo1 and geo2 differ and there are regions at the edges with large difference in overlap.

Usage

check_tongfen_single_areas(geo1, geo2, correspondence)

Arguments

Value

A table with columns 'TongfenID', 'area1' and 'area2', where each row corresponds to a unique 'TongfenID' from them 'correspondence' table and the other columns hold the areas of the regions aggregated from 'geo1' and 'geo2'.'

Generate togfen correspondence for list of geographies

Description

Get correspondence data for arbitrary congruent geometries. Congruent means that one can obtain a common tiling by aggregating several sub-geometries in each of the two input geo data. Worst case scenario the only common tiling is given by unioning all sub-geometries and there is no finer common tiling.

Usage

estimate_tongfen_correspondence(
  data,
  geo_identifiers,
  method = "estimate",
  tolerance = 50,
  computation_crs = NULL
)

Arguments

Value

A correspondence table linking geo1_uid and geo2_uid with unique TongfenID and TongfenUID columns that enumerate the common geometry.

Examples

# Estimate a common geography for 2006 and 2016 dissemination areas in the City of Vancouver
# based on the geographic data.
## Not run: 
regions <- list(CSD="5915022")

data_06 <- cancensus::get_census("CA06",regions=regions,geo_format='sf',level="DA") %>%
 rename(GeoUID_06=GeoUID)
data_16 <- cancensus::get_census("CA16",regions=regions,geo_format="sf",level="DA") %>%
  rename(GeoUID_16=GeoUID)

correspondence <- estimate_tongfen_correspondence(list(data_06, data_16),
                                                  c("GeoUID_06","GeoUID_16"))


## End(Not run)

Generate togfen correspondence for two geographies

Description

Get correspondence data for arbitrary congruent geometries. Congruent means that one can obtain a common tiling by aggregating several sub-geometries in each of the two input geo data. Worst case scenario the only common tiling is given by unioning all sub-geometries and there is no finer common tiling.

Usage

estimate_tongfen_single_correspondence(
  geo1,
  geo2,
  geo1_uid,
  geo2_uid,
  tolerance = 1,
  computation_crs = NULL,
  robust = FALSE
)

Arguments

Value

A correspondence table linking geo1_uid and geo2_uid with unique TongfenID and TongfenUID columns that enumerate the common geometry.

Get StatCan DA or DB level correspondence file

Description

Joins the StatCan correspodence files for several census years

Usage

get_correspondence_ca_census_for(years, level, refresh = FALSE)

Arguments

Value

tibble with correspondence table'spanning all years

Get StatCan DA or DB level correspondence file

Description

Usage

get_single_correspondence_ca_census_for(
  year,
  level = c("DA", "DB"),
  refresh = FALSE
)

Arguments

Value

tibble with correspondence table'

Togfen data from several Canadian censuses

Description

Get data from several Candian censuses on a common geography. Requires sf and cancensus package to be available

Usage

get_tongfen_ca_census(
  regions,
  meta,
  level = "CT",
  method = "statcan",
  base_geo = NULL,
  na.rm = FALSE,
  tolerance = 50,
  area_mismatch_cutoff = 0.1,
  quiet = FALSE,
  refresh = FALSE,
  crs = NULL,
  data_transform = function(d) d
)

Arguments

Value

dataframe with variables on common geography

Examples

# Get rent data for census years 2001 through 2016
## Not run: 
rent_variables <- c(rent_2001="v_CA01_1667",rent_2016="v_CA16_4901",
                    rent_2011="v_CA11N_2292",rent_2006="v_CA06_2050")
meta <- meta_for_ca_census_vectors(rent_variables)

regions=list(CMA="59933")
rent_data <- get_tongfen_ca_census(regions=regions, meta=meta, quiet=TRUE,
                                   method="estimate", level="CT", base_geo = "CA16")


## End(Not run)

Canadian census CT level tongfen via DA correspondence

Description

Grab variables from several censuses on a common geography. Requires sf package to be available Will return CT level data

Usage

get_tongfen_ca_census_ct_from_da(
  regions,
  vectors,
  geo_format = NA,
  use_cache = TRUE,
  na.rm = TRUE,
  quiet = TRUE
)

Arguments

Value

dataframe with variables on common geography

Canadian census CT level tongfen

Description

Grab variables from several censuses on a common geography. Requires sf package to be available Will return CT level data

Usage

get_tongfen_census_ct(
  regions,
  vectors,
  geo_format = NA,
  na.rm = TRUE,
  quiet = TRUE,
  refresh = FALSE
)

Arguments

Value

dataframe with census variables on common geography

Canadian Census DA level tongfen

Description

Grab variables from several censuses on a common geography. Requires sf package to be available Will return CT level data

Usage

get_tongfen_census_da(
  regions,
  vectors,
  geo_format = NA,
  use_cache = TRUE,
  na.rm = TRUE,
  quiet = TRUE
)

Arguments

Value

dataframe with variables on common geography

Get StatCan correspondence data

Description

Get correspondence file for several Candian censuses on a common geography. Requires sf and cancensus package to be available

Usage

get_tongfen_correspondence_ca_census(
  geo_datasets,
  regions,
  level = "CT",
  method = "statcan",
  tolerance = 50,
  area_mismatch_cutoff = 0.1,
  quiet = FALSE,
  refresh = FALSE
)

Arguments

Value

dataframe with the multi-census correspondence file

Examples

# Get correspondance files between CTs in 2006 and 2016 censuses in Vancouver CMA
## Not run: 
correspondence <- get_tongfen_correspondence_ca_census(geo_datasets=c('CA06','CA16'),
                                                       regions=list(CMA="59933"),level='CT')

## End(Not run)

Get US census data for 2000 and 2010 census on common census tract based geography

Description

This wraps data acquisition via the tidycensus package and tongfen on a common geography into a single convenience function.

Usage

get_tongfen_us_census(
  regions,
  meta,
  level = "tract",
  survey = "census",
  base_geo = NULL
)

Arguments

Value

sf object with (wide form) census variables with census year as suffix (separated by underdcore "_").

Examples

# Get US census data on population and households for 2000 and 2010 censuses on a uniform geography
# based on census tracts.
## Not run: 
variables=c(population="H011001",households="H013001")

meta <- c(2000,2010) %>%
  lapply(function(year){
    v <- variables %>% setNames(paste0(names(.),"_",year))
    meta_for_additive_variables(paste0("dec",year),v)
  }) %>%
  bind_rows()
census_data <- get_tongfen_us_census(regions = list(state="CA"), meta=meta, level="tract") %>%
  mutate(change=population_2010/households_2010-population_2000/households_2000)


## End(Not run)

Generate tongfen metadata for additive variables

Description

Generates metadata to be used in tongfen_aggregate. Variables need to be additive like counts.

Usage

meta_for_additive_variables(dataset, variables)

Arguments

Value

a tibble to be used in tongfen_aggregate

Examples

# Get metadata for additive variable Population for the CA16 and CA06 datasets
## Not run: 
meta <- meta_for_additive_variables(c("CA06","CA16"),"Population")

## End(Not run)

Generate metadata from Candian census vectors

Description

Build tibble with information on how to aggregate variables given vectors Queries list_census_variables to obtain needed information and add in vectors needed for aggregation

Usage

meta_for_ca_census_vectors(vectors)

Arguments

Value

tidy dataframe with metadata information for requested variables and additional variables needed for tongfen operations

Examples

# Build metadata for vectors
## Not run: 
meta <- meta_for_ca_census_vectors("v_CA16_4836","v_CA16_4838","v_CA16_4899")

## End(Not run)

Dasymetric downsampling

Description

Proportionally re-aggregate hierarchical data to lower-level w.r.t. values of the *base* variable Also handles cases where lower level data may be available but blinded at times by filling in data from higher level

Data at lower aggregation levels may not add up to the more accurate aggregate counts. This function distributes the aggregate level counts proportionally (by population) to the containing lower level geographic regions.

Usage

proportional_reaggregate(
  data,
  parent_data,
  geo_match,
  categories,
  base = "Population"
)

Arguments

Value

dataframe with downsampled variables from parent_data

Examples

# Proportionally reaggregate visible minority data from dissemination area 2016
# census data to dissemination block geography, proportionally based on dissemination
# block population
## Not run: 
regions <- list(CSD="5915022")
variables <- cancensus::child_census_vectors("v_CA16_3954")

da_data <- cancensus::get_census("CA16",regions=regions,
                                 vectors=setNames(variables$vector,variables$label),
                                 level="DA")
geo_data <- cancensus::get_census("CA16",regions=regions,geo_format="sf",level="DB")

db_data <- geo_data %>% proportional_reaggregate(da_data,c("DA_UID"="GeoUID"),variables$label)


## End(Not run)

Perform tongfen according to correspondence

Description

Aggregate variables secified in meta for several datasets according to correspondence.

Usage

tongfen_aggregate(data, correspondence, meta = NULL, base_geo = NULL)

Arguments

Value

aggregated dataset of class sf if base_geo is not NULL and data is of type sf or tibble otherwise.

Examples

# aggregate census tract level 2006 population data on common gepgraphy build through
# correspondence from 2006 and 2016 census tracts in the City of Vancouver.
## Not run: 
regions <- list(CSD="5915022")
geo1 <- cancensus::get_census("CA06",regions=regions,geo_format='sf',level='CT')
geo2 <- cancensus::get_census("CA16",regions=regions,geo_format='sf',level='CT')
meta <- meta_for_additive_variables("CA06","Population")
correspondence <- get_tongfen_correspondence_ca_census(geo_datasets=c('CA06','CA16'),
                                                       regions=regions,level='CT')
result <- tongfen_aggregate(list(geo1 %>% rename(GeoUIDCA06=GeoUID),
                                 geo2 %>% rename(GeoUIDCA16=GeoUID)),correspondence,meta)

## End(Not run)

Canadian census CT level tongfen via identifier matching

Description

Aggregate variables to common CTs, returns data2 on new tiling matching data1 geography

Usage

tongfen_ca_census_ct(
  data1,
  data2,
  data2_sum_vars,
  data2_group_vars = c(),
  na.rm = TRUE
)

Arguments

Estimate variable values for custom geography

Description

Estimates data from source geometry onto target geometry using area-weighted interpolation. The metadata specifies how data should be aggregated, "additive" data like population counts are summed up proportionally to the area of the intersection, "averages" need further additive "parent" count variables to estimate weighted averages.

Usage

tongfen_estimate(target, source, meta, na.rm = FALSE)

Arguments

Value

'target' with estimated quantities from 'source' as specified by 'meta'

Examples

# Estimate 2006 Population in the City of Vancouver dissemination ares on 2016 census geographies
## Not run: 
geo1 <- cancensus::get_census("CA06",regions=list(CSD="5915022"),geo_format='sf',level='DA')
geo2 <- cancensus::get_census("CA16",regions=list(CSD="5915022"),geo_format='sf',level='DA')
meta <- meta_for_additive_variables("CA06","Population")
result <- tongfen_estimate(geo2 %>% rename(Population_2016=Population),geo1,meta)

## End(Not run)

Tongfen estimate data for given geometry

Description

Estimates values for the given census vectors for the given geometry using data from the specified level range. This is a wrapper around 'cancensus::get_intersecting_geometries' and 'tongfen_estimate', optionally with downsampling via 'proportional_reaggregate', to streamline estimating Canadian census data on custom geographies.

Usage

tongfen_estimate_ca_census(
  geometry,
  meta,
  level,
  intersection_level = level,
  downsample_level = NULL,
  na.rm = FALSE,
  quiet = FALSE
)

Arguments

Examples

# Estimate a common geography for 2006 and 2016 dissemination areas in the City of Vancouver
# based on the geographic data and check estimation errors
## Not run: 
toronto_city_hall <- sf::st_point(c(-79.3839,43.6534)) %>%
  sf::st_sfc(crs=4326) %>%
  sf::st_transform(3348) %>%
  sf::st_buffer(1000) %>%
  sf::st_sf()

meta <- meta_for_additive_variables("CA16","Population")

data <- tongfen_estimate_ca_census(toronto_city_hall,meta,level="DA",intersection_level="CT")

print(paste0("Approximately ",scales::comma(data$Population,accuracy=100),
             " people live within a 1 km radius of Toronto City."))


## End(Not run)

Tag regions by largest overlap

Description

tags regions in 'source' by 'target_id' of region in 'target' with the largest overlap

Usage

tongfen_tag_largest_overlap(source, target, target_id)

Arguments

Value

'source' with extra column with name '"target_id"' and column '...overlap_fraction' with the proportion of overlap of the target geometry with the respective 'target_id'

Examples

# Estimate 2006 Populatino in the City of Vancouver dissemination ares on 2016 census geoographies
## Not run: 
geo1 <- cancensus::get_census("CA06",regions=list(CSD="5915022"),geo_format='sf',level='DA')
geo2 <- cancensus::get_census("CA16",regions=list(CSD="5915022"),geo_format='sf',level='DA')
meta <- meta_for_additive_variables("CA06","Population")
result <- tongfen_estimate(geo2 %>% rename(Population_2016=Population),geo1,meta)

## End(Not run)

A dataset with polling station votes data from the 2015 federal election in the Vancouver area

Description

A dataset with polling station votes data from the 2015 federal election in the Vancouver area

Author(s)

Elections Canada

References

https://www.elections.ca/content.aspx?section=res&dir=rep/off&document=index&lang=e#42GE

A dataset with polling station votes data from the 2019 federal election in the Vancouver area

Description

A dataset with polling station votes data from the 2019 federal election in the Vancouver area

Author(s)

Elections Canada

References

https://www.elections.ca/content.aspx?section=res&dir=rep/off&document=index&lang=e#43GE

A dataset with polling district geographies from the 2015 federal election in the Vancouver area

Description

A dataset with polling district geographies from the 2015 federal election in the Vancouver area

Author(s)

Elections Canada

References

https://www.elections.ca/content.aspx?section=res&dir=rep/off&document=index&lang=e#42GE

A dataset with polling district geographies from the 2019 federal election in the Vancouver area

Description

A dataset with polling district geographies from the 2019 federal election in the Vancouver area

Author(s)

Elections Canada

References

https://www.elections.ca/content.aspx?section=res&dir=rep/off&document=index&lang=e#43GE