Package {tongfen}

Generate metadata from Canadian 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 integrity

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.

The result is a diagnostic, not a pass/fail test. Geographies for different years are simplified independently and differ in how water features are cut out, so a sizable area mismatch does not by itself mean the regions were matched up incorrectly.

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 integrity

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 tongfen 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 tongfen 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 correspondence 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

The correspondence files are downloaded from a mirror of the Statistics Canada correspondence files and cached in the tongfen cache directory. The cached files are checked against the mirror once per session and get downloaded again if they changed. The location of the mirror can be changed via the 'tongfen.statcan_correspondence_url' option.

Usage

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

Arguments

Value

tibble with correspondence table'

Tongfen data from several Canadian censuses

Description

Get data from several Canadian 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,
  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 Canadian 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,
  quiet = FALSE,
  refresh = FALSE,
  crs = 3347
)

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 correspondence table for US census geographies

Description

Builds a correspondence table matching US census geographies across censuses, based on the relationship files published by the US Census Bureau. Censuses that aren't requested but sit in between two that are get traversed on the way, the Census Bureau only publishes relationship files between consecutive censuses.

The relationship files are geometric overlays that list every sliver along boundaries that only shifted slightly. Those get cut via 'min_area_share', keeping them would chain unrelated regions into one common geography.

The correspondence layer reaches back one census further than get_tongfen_us_census. The 1990 census is available as 'dec1990' here, but the Census Bureau has retired the 1990 API endpoint, so 1990 data has to be brought in by other means, for example from NHGIS via the ipumsr package, and handed to tongfen_aggregate together with this correspondence table.

Usage

get_tongfen_correspondence_us_census(
  datasets,
  regions,
  level = "tract",
  min_area_share = 0.01,
  cache_path = getOption("tongfen.cache_path")
)

Arguments

Value

tibble with one row per census geography, a GEOID column for each requested census, and the common geography identified by 'TongfenID' and 'TongfenUID'.

Examples

# Match up census tracts for the 1990 and 2000 censuses in Rhode Island
## Not run: 
correspondence <- get_tongfen_correspondence_us_census(datasets = c("dec1990","dec2000"),
                                                       regions = list(state="RI"))

## End(Not run)

Get US census data for several censuses on a common geography

Description

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

Data is only available for the 2000, 2010 and 2020 censuses, the Census Bureau has retired the 1990 API endpoint. To tongfen 1990 data, obtain it elsewhere and combine it with a correspondence table from get_tongfen_correspondence_us_census via tongfen_aggregate.

Usage

get_tongfen_us_census(
  regions,
  meta,
  level = "tract",
  survey = "census",
  base_geo = NULL,
  min_area_share = 0.01,
  sumfile = NULL
)

Arguments

Value

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

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 Canadian 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(c("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 specified in meta for several datasets according to correspondence.

Usage

tongfen_aggregate(
  data,
  correspondence,
  meta = NULL,
  base_geo = NULL,
  na.rm = TRUE
)

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 and 2016 population data on common geography built 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(c("CA06","CA16"),"Population")
correspondence <- get_tongfen_correspondence_ca_census(geo_datasets=c('CA06','CA16'),
                                                       regions=regions,level='CT')
result <- tongfen_aggregate(list(CA06=geo1 %>% rename(GeoUIDCA06=GeoUID),
                                 CA16=geo2 %>% rename(GeoUIDCA16=GeoUID)),correspondence,meta)

## End(Not run)

Determine regions to join to correct for likely geocoding anomalies

Description

Looks for regions with surprising drops in the timeline of a count variable that are complemented by a neighbouring region, as explained in 'tongfen_detect_anomalies', and joins them. This gets repeated on the joined regions until there are no more regions left that qualify to get joined. In each round a region only gets joined with one other region, the most surprising regions go first.

Joining regions trades geographic detail for timelines that are consistent over time. The parameters control how aggressively regions get joined and are best calibrated on the data at hand, erring on the side of joining too few regions risks keeping geocoding problems, erring on the other side risks removing real changes and needlessly coarsens the geography.

The result can be used to join the regions via 'tongfen_join_regions', or to update a correspondence via 'tongfen_join_correspondence'.

Usage

tongfen_anomaly_joins(
  data,
  variables,
  id = "TongfenID",
  neighbours = NULL,
  rel_scale = 0.25,
  abs_scale = 200,
  p = 4,
  surprise_cutoff = 0.15,
  total_surprise_cutoff = 0.75,
  cutoff_fact = 0.6,
  surprise_reduction_const = 0.15,
  sum_fact = 0.7
)

Arguments

Value

A tibble with one row for each region that gets joined with other regions, with the identifier of the region, the identifier of the joined region it becomes part of in the column named like the identifier with suffix '_joined', by default 'TongfenID_joined', and the 'round' in which the region first got joined to another region. The identifier of a joined region is the smallest identifier of the regions it is made up of.

Examples

# Correct 2001 through 2021 dissemination area level population timelines in the
# City of Vancouver for likely geocoding problems
## Not run: 
datasets <- c("CA01","CA06","CA11","CA16","CA21")
meta <- meta_for_additive_variables(datasets,"Population")
data <- get_tongfen_ca_census(regions=list(CSD="5915022"),meta=meta,level="DA",base_geo="CA21")

joins <- tongfen_anomaly_joins(data,paste0("Population_",datasets))
corrected_data <- tongfen_join_regions(data,joins,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

Value

'data2' with the variables in 'data2_sum_vars' aggregated to a common geography matching 'data1', identified by the 'GeoUID' of 'data1'

Detect likely geocoding anomalies in timelines on a common geography

Description

TongFen is only as good as the geocoding that assigned the underlying data to geographic regions in the first place. Geocoding varies over time, and the same dwelling units, and the people living in them, can get assigned to different neighbouring regions in different years. In a timeline on a common geography this shows up as a surprising drop in one region that is offset by a corresponding jump in a neighbouring region.

This function lists the candidate regions with surprising drops in the given count variable, together with the neighbouring region that takes away most of the surprise when both are joined. Use it to check for possible problems and to calibrate the parameters before joining regions with 'tongfen_anomaly_joins'. Not all surprising drops are due to geocoding problems, a drop that is not complemented by a neighbouring region is likely real.

The surprise of a change between two consecutive years ranges from 0 to 1. Only decreases are surprising, the surprise is the product of the surprise of the relative and of the absolute decrease, so that it takes a decrease that is large in both relative and absolute terms to be surprising. The total surprise of a region is the 'p'-norm of the surprises across all changes in the timeline.

A candidate region and its neighbour are flagged for joining if joining reduces the total surprise of the candidate region to below 'cutoff_fact' times its total surprise, or reduces it by more than 'surprise_reduction_const', or reduces it to below 'cutoff_fact * sum_fact' times the sum of the total surprises of both regions. To keep this comparable the surprise after joining is computed from the change of the joined regions relative to the counts of the candidate region alone.

Usage

tongfen_detect_anomalies(
  data,
  variables,
  id = "TongfenID",
  neighbours = NULL,
  rel_scale = 0.25,
  abs_scale = 200,
  p = 4,
  surprise_cutoff = 0.15,
  total_surprise_cutoff = 0.75,
  cutoff_fact = 0.6,
  surprise_reduction_const = 0.15,
  sum_fact = 0.7
)

Arguments

Value

A tibble with one row for each candidate region, most surprising first, with the identifier of the region, the number of surprising changes 'surprise_count', the total surprise 'surprise_total', the 'period' with the most surprising change, the identifier of the 'neighbour' that takes away most of the surprise, the total surprise 'surprise_total_joined' after joining both and 'join' indicating if both regions qualify to get joined.

Examples

# Check 2001 through 2021 dissemination area level population timelines in the
# City of Vancouver for possible geocoding problems
## Not run: 
datasets <- c("CA01","CA06","CA11","CA16","CA21")
meta <- meta_for_additive_variables(datasets,"Population")
data <- get_tongfen_ca_census(regions=list(CSD="5915022"),meta=meta,level="DA",base_geo="CA21")

anomalies <- tongfen_detect_anomalies(data,paste0("Population_",datasets))

## End(Not run)

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', regions in 'target' that don't overlap with ‘source' have 'NA' values. Columns in 'target' can’t have the same name as the variables to be estimated.

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

Value

'geometry' with the estimated values for the census variables specified by 'meta'

Examples

# Estimate the 2016 population within 1 km of Toronto City Hall from dissemination area level
# census data
## 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 Hall."))


## End(Not run)

Join regions in a correspondence

Description

Updates a correspondence so that the given regions are joined, for example to correct for likely geocoding anomalies as determined by 'tongfen_anomaly_joins'. The updated correspondence can be used in 'tongfen_aggregate' to aggregate data on the coarser common geography, which works for all variables 'tongfen_aggregate' can deal with and for data that was not part of detecting the anomalies.

Usage

tongfen_join_correspondence(correspondence, joins)

Arguments

Value

The correspondence with updated TongfenID and TongfenUID for the regions that got joined. If the correspondence has a TongfenMethod column "anomaly" gets added to the method of the regions that got joined.

Examples

# Correct for likely geocoding problems in dissemination area level population timelines
# and use the updated correspondence to aggregate data on the corrected common geography
## Not run: 
regions <- list(CSD="5915022")
datasets <- c("CA01","CA06","CA11","CA16","CA21")
meta <- meta_for_additive_variables(datasets,"Population")
data <- get_tongfen_ca_census(regions=regions,meta=meta,level="DA",base_geo="CA21")
joins <- tongfen_anomaly_joins(data,paste0("Population_",datasets))

correspondence <- get_tongfen_correspondence_ca_census(geo_datasets=datasets,
                                                       regions=regions,level="DA") %>%
  tongfen_join_correspondence(joins)

## End(Not run)

Join regions in data on a common geography

Description

Joins regions in data that has already been aggregated to a common geography, for example to correct for likely geocoding anomalies as determined by 'tongfen_anomaly_joins'. The data, and the geometries if the data is of class sf, of the regions that get joined are aggregated, all other regions are left as they are.

Variables are aggregated according to the metadata, numeric variables that are not part of the metadata are assumed to be additive. Variables that are not additive, like averages, can only be aggregated if their parent variable is part of the data. If that is not the case use 'tongfen_join_correspondence' to update the correspondence the data was built from and aggregate the original data again with 'tongfen_aggregate'.

Usage

tongfen_join_regions(data, joins, meta = NULL, id = "TongfenID", na.rm = TRUE)

Arguments

Value

The data with the regions joined. Joined regions take the place and the identifier of the region with the smallest identifier among the regions they are made up of. Variables that are not numeric and not part of the metadata are 'NA' for joined regions.

Examples

# Correct 2001 through 2021 dissemination area level population timelines in the
# City of Vancouver for likely geocoding problems
## Not run: 
datasets <- c("CA01","CA06","CA11","CA16","CA21")
meta <- meta_for_additive_variables(datasets,"Population")
data <- get_tongfen_ca_census(regions=list(CSD="5915022"),meta=meta,level="DA",base_geo="CA21")

joins <- tongfen_anomaly_joins(data,paste0("Population_",datasets))
corrected_data <- tongfen_join_regions(data,joins,meta)

## 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 the name given by 'target_id' and column '...overlap_fraction' with the proportion of the area of the source region that overlaps with the region in 'target' with that id

Examples

# Tag 2016 dissemination areas in the City of Vancouver by the 2006 census tract they overlap
# the most with
## Not run: 
geo1 <- cancensus::get_census("CA06",regions=list(CSD="5915022"),geo_format='sf',level='CT')
geo2 <- cancensus::get_census("CA16",regions=list(CSD="5915022"),geo_format='sf',level='DA')
result <- tongfen_tag_largest_overlap(geo2,geo1 %>% select(CT_2006=GeoUID),"CT_2006")

## 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