The goal of parcel is to provide tools for matching real-world addresses to reference sets of addresses; e.g., “352 Helen Street”, “352 Helen St.” or “352 helen st”. This package is motivated by the included example data resources of auditor parcel tax data from Hamilton County, Ohio. Use get_parcel_data() to get the corresponding parcel data for a vector of addresses:
get_parcel_data(
c("1069 Overlook Avenue Cincinnati OH 45238",
"419 Elm St. Cincinnati OH 45238",
"3333 Burnet Ave Cincinnati OH 45219",
"3830 President Drive Cincinnati Ohio 45225",
"3544 Linwood Av Cincinnati OH 45226")
)
#> # A tibble: 5 × 22
#> input_address parcel_id score centroid_lat centroid_lon parcel_address
#> <chr> <chr> <dbl> <dbl> <dbl> <chr>
#> 1 1069 Overlook Avenu… 1800A800… 0.795 39.1 -84.6 1069 OVERLOOK…
#> 2 419 Elm St. Cincinn… 54000410… 0.860 39.1 -84.6 419 ELM ST
#> 3 3333 Burnet Ave Cin… nonres-c… 0.860 NA NA <NA>
#> 4 3830 President Driv… president NA NA NA <NA>
#> 5 3544 Linwood Av Cin… 01900010… 0.860 39.1 -84.4 3544 LINWOOD …
#> # ℹ 16 more variables: parcel_addr_number <chr>, parcel_addr_street <chr>,
#> # parcel_addr_suffix <chr>, land_use <fct>, condo_id <chr>, condo_unit <chr>,
#> # market_total_value <dbl>, acreage <dbl>, homestead <lgl>,
#> # rental_registration <lgl>, year_built <dbl>, n_total_rooms <dbl>,
#> # n_bedrooms <dbl>, n_full_bathrooms <dbl>, n_half_bathrooms <dbl>,
#> # online_market_total_value <dbl>With this specific goal in mind, parcel includes:
- functions for cleaning and tagging components of addresses:
clean_address(),tag_address(), andcreate_address_stub() - the
cagis_parcelstabular-data-resource, which contains parcel identifiers, parcel addresses, and parcel characteristics downloaded from the Cincinnati Area Geographic Information System (CAGIS) - the
hamilton_online_parcelstabular-data-resource, which contains parcel characteristics scraped from Hamilton County Auditor Online - functions for joining addresses to parcel identifiers based on an included model pretrained on electronic health record addresses in Hamilton County, OH and a list of custom pseudo-identifiers for multi-building apartment complexes:
link_parcel(),link_apt()
Installation
The development version of parcel can be installed with:
pak::pak("geomarker-io/parcel"){parcel} requires the usaddress python module for tagging addresses and creating address stubs, as well as the dedupe and dedupe-variable-address python modules for matching addresses, all of which can be installed to an existing python environment from inside R using reticulate:
reticulate::py_install("usaddress", pip = TRUE)
reticulate::py_install("dedupe", pip = TRUE)
reticulate::py_install("dedupe-variable-address", pip = TRUE)If there is no preconfigured or compatible version of Python already available on the system, Miniconda will be installed and the required Python packages will be installed in the standard shared environment for R sessions (typically a virtual environment, or a Conda environment named “r-reticulate”) after library(parcel) is called.
Identifiers for Parcels and Properties
A parcel_id refers to the Hamilton County Auditor’s “Parcel Number”, which is referred to as the “Property Number” within the CAGIS Open Data and uniquely identifies properties. In rare cases, multple addresses can share the same parcel boundaries, but have unique parcel_ids and in these cases, their resulting centroid coordinates would also be identical.
Within the process of matching to a parcel, an individual address could be merged with differing types and resolutions of data:
%%{init: { "fontFamily": "arial" } }%%
flowchart LR
classDef id fill:#fff,stroke:#000,stroke-width:1px;
classDef tool fill:#e8e8e8,stroke:#000,stroke-width:1px,stroke-dasharray: 5 2;
classDef data fill:#fff,stroke:#000,stroke-width:1px;
addr(hospitalization):::id ---> hc("likely in \nHamilton County \n (by ZIP code)"):::data
addr ---> nhc("not in Hamilton County"):::tool
hc --> inst[institutional parcel]:::id
inst -. "institution 'type' linkage\n (e.g., JFS, CCHMC, RMH)" .-> sdoh("temporary housing,\n foster care,\n low income housing tax credit"):::data
hc --> res(residential parcel):::id
res -- CCHMC \nlinkage --> hhh("home's hospitalization history \n (i.e., pedigree)"):::data
res -- CAGIS & \nODC linkage --> hce(housing code enforcement,\n public service calls, crime):::data
res -- single family dwelling --> vat("family-level SES measures \n (e.g., value, age, condition, tenure)"):::data
res -- multi-family dwelling --> lu("auditor land use type \n (e.g., two family dwelling, \n apartment with 20-39 units)"):::data
hc --> npm(not matched \nto a parcel):::toolNon-Residential Parcels
Known non-residential addresses will be matched and returned with a special parcel identifer denoting that the matched parcel is non-residential; e.g., Cincinnati Children’s Hospital Medical Center, Jobs and Family Services, Ronald McDonald House):
get_parcel_data(
c("222 E Central Parkway Cincinnati Ohio 45220",
"222 central pkwy Cincinnati Ohio 45220",
"3333 Burnet Ave Cincinnati Ohio 45219",
"3333 Burnet Avenue Cincinnati Ohio 45219",
"350 Erkenbrecher Ave Cincinnati Ohio 45219")
) |>
dplyr::select(input_address, parcel_id)
#> # A tibble: 5 × 2
#> input_address parcel_id
#> <chr> <chr>
#> 1 222 E Central Parkway Cincinnati Ohio 45220 nonres-jfs
#> 2 222 central pkwy Cincinnati Ohio 45220 nonres-jfs
#> 3 3333 Burnet Ave Cincinnati Ohio 45219 nonres-cchmc
#> 4 3333 Burnet Avenue Cincinnati Ohio 45219 nonres-cchmc
#> 5 350 Erkenbrecher Ave Cincinnati Ohio 45219 nonres-rmh-350Condominiums
Because “second line” address components (e.g., “Unit 2B”) are not captured, a single address can refer to multiple parcels in the case of condos or otherwise shared building ownership. For example, the address “323 Fifth St” has six distinct parcel_ids, each with different home values and land uses:
| parcel_id | market_total_value | land_use |
|---|---|---|
| 14500010321 | 397500 | condominium unit |
| 14500010317 | 123000 | condominium office building |
| 14500010320 | 180000 | condominium unit |
| 14500010319 | 255000 | condominium unit |
| 14500010322 | 388230 | condominium unit |
| 14500010318 | 239500 | condominium unit |
In this case, a special parcel identifier TIED_MATCH is returned to denote that the address matched more than one parcel:
get_parcel_data("323 Fifth St W Cincinnati OH 45202")$parcel_id
#> [1] "TIED_MATCHES"Large Apartment Complexes
Large apartment complexes often use multiple mailing addresses that are not the same as the parcel address(es). In these special cases, link_apt() is used to match addresses exactly based on their street name if the street number falls within a certain range:
str(parcel:::apt_defs)
#> List of 9
#> $ president :List of 3
#> ..$ street_name: chr [1:2] "president drive" "president dr"
#> ..$ range_low : num 3000
#> ..$ range_high : num 4999
#> $ tower :List of 3
#> ..$ street_name: chr [1:4] "east tower drive" "east tower dr" "e tower drive" "e tower dr"
#> ..$ range_low : num 2000
#> ..$ range_high : num 29999
#> $ bahama :List of 3
#> ..$ street_name: chr [1:4] "bahama terrace" "bahama te" "bahama ter" "bahama terr"
#> ..$ range_low : num 5000
#> ..$ range_high : num 5999
#> $ hawaiian :List of 3
#> ..$ street_name: chr [1:4] "hawaiian terrace" "hawaiian te" "hawaiian ter" "hawaiian terr"
#> ..$ range_low : num 4000
#> ..$ range_high : num 5999
#> $ dewdrop :List of 3
#> ..$ street_name: chr [1:2] "dewdrop circle circle" "dewdrop circle cir"
#> ..$ range_low : num 400
#> ..$ range_high : num 599
#> $ winneste :List of 3
#> ..$ street_name: chr [1:3] "winneste avenue" "winneste ave" "winneste av"
#> ..$ range_low : num 4000
#> ..$ range_high : num 5999
#> $ walden_glen:List of 3
#> ..$ street_name: chr [1:2] "walden glen circle" "walden glen cir"
#> ..$ range_low : num 2000
#> ..$ range_high : num 2999
#> $ clovernook :List of 3
#> ..$ street_name: chr [1:3] "clovernook avenue" "clovernook ave" "clovernook av"
#> ..$ range_low : num 7000
#> ..$ range_high : num 7999
#> $ nottingham :List of 3
#> ..$ street_name: chr [1:4] "nottingham road" "nottingham rd" "nottingham drive" "nottingham dr"
#> ..$ range_low : num 2000
#> ..$ range_high : num 2999
get_parcel_data("5377 Bahama Ter Cincinnati Ohio 45223")$parcel_id
#> [1] "bahama"CAGIS Parcels Data
The cagis_parcels tabular data resource (TDR) is created using the R scripts in /inst and stored within the package. It can be loaded using {fr}:
d_parcel <- fr::read_fr_tdr(fs::path_package("parcel", "cagis_parcels"))
d_parcel
#> cagis_parcels
#> - version: 0.11.0
#> - title: CAGIS Parcels
#> - homepage: <https://github.com/geomarker-io/hamilton_parcels>
#> - description: A curated property-level data resource derived from the Hamilton
#> County, OH Auditor data distributed through CAGIS Open Data:
#> https://cagismaps.hamilton-co.org/cagisportal/mapdata/download
#> # A tibble: 259,653 × 14
#> parcel_id centroid_lat centroid_lon parcel_address parcel_addr_number
#> <chr> <dbl> <dbl> <chr> <chr>
#> 1 6210023015500 39.3 -84.3 184 CANNONADE DR 184
#> 2 6210023017000 39.3 -84.3 154 THOROBRED RD 154
#> 3 6210023017700 39.3 -84.3 112 CITATION CT 112
#> 4 6210023018100 39.3 -84.3 119 CITATION CT 119
#> 5 6210023018500 39.3 -84.3 112 THOROBRED RD 112
#> 6 6210023020300 39.3 -84.3 242 CORDERO TL 242
#> 7 6210023020500 39.3 -84.3 254 CORDERO TL 254
#> 8 6210023022800 39.3 -84.3 212 RIVA RIDGE CT 212
#> 9 6210023023100 39.3 -84.3 230 RIVA RIDGE CT 230
#> 10 6210023023300 39.3 -84.3 242 RIVA RIDGE CT 242
#> # ℹ 259,643 more rows
#> # ℹ 9 more variables: parcel_addr_street <chr>, parcel_addr_suffix <chr>,
#> # land_use <fct>, condo_id <chr>, condo_unit <chr>, market_total_value <dbl>,
#> # acreage <dbl>, homestead <lgl>, rental_registration <lgl>
d_parcel@schema
#> parcel_id
#> - type: string
#> - description: uniquely identifies properties; the auditor Parcel Number
#> centroid_lat
#> - type: number
#> - description: calculated as centroid of casted multipolygon geometry and
#> projected from Ohio South to WGS84
#> centroid_lon
#> - type: number
#> - description: calculated as centroid of casted multipolygon geometry and
#> projected from Ohio South to WGS84
#> parcel_address
#> - type: string
#> - description: derived by pasting parcel_address_{number, street, suffix}`
#> together
#> parcel_addr_number
#> - type: string
#> parcel_addr_street
#> - type: string
#> parcel_addr_suffix
#> - type: string
#> land_use
#> - type: string
#> - constraints: enum = apartment, 4-19 units, apartment, 20-39 units, apartment,
#> 40+ units, mobile home / trailer park, other commercial housing, office /
#> apartment over, single family dwelling, two family dwelling, three family
#> dwelling, condominium unit, boataminium, condo or pud garage, landominium,
#> manufactured home, lihtc res, other residential structure, and metropolitan
#> housing authority
#> condo_id
#> - type: string
#> - description: used to match two parcels to the same building of condos
#> condo_unit
#> - type: string
#> market_total_value
#> - type: number
#> acreage
#> - type: number
#> homestead
#> - type: boolean
#> rental_registration
#> - type: boolean
# without fr:
# read.csv(fs::path_package("parcel", "cagis_parcels"))Auditor parcel-level data were excluded if they (1) did not contain a parcel identifier, (2) did not contain a property address number/name, or (3) had a duplicated parcel identifier.
Parcels with the following land use categories are included in the data resource and others are excluded. These were selected to reflect residential usages of parcels.
library(dplyr, warn.conflicts = FALSE)
d_parcel |>
group_by(land_use) |>
summarize(n_parcels = n()) |>
arrange(desc(n_parcels)) |>
knitr::kable()| land_use | n_parcels |
|---|---|
| single family dwelling | 212995 |
| condominium unit | 20325 |
| two family dwelling | 11331 |
| apartment, 4-19 units | 5654 |
| landominium | 3049 |
| three family dwelling | 1854 |
| condo or pud garage | 1063 |
| other residential structure | 869 |
| metropolitan housing authority | 744 |
| apartment, 40+ units | 619 |
| apartment, 20-39 units | 458 |
| manufactured home | 204 |
| office / apartment over | 187 |
| boataminium | 141 |
| other commercial housing | 95 |
| mobile home / trailer park | 40 |
| lihtc res | 25 |
Some of the parcel characteristics do not make sense in certain contexts and should not be interpreted incorrectly; for example, the value of a parcel for a multi-family or multi-unit housing structure shouldn’t be compared to the value of a parcel for a single-family household for the purposes of assesing individual-level SES.
Hamilton County Auditor Online Data
The hamilton_online_parcels TDR is created by linking a saved scraping of the auditor’s website to the parcel identifiers in the cagis_parcels TDR.
Similarly, the hamilton_online_parcel TDR is created using the R scripts in /inst and stored within the package. It can be loaded using {fr}:
d_online <- fr::read_fr_tdr(fs::path_package("parcel", "hamilton_online_parcels"))
d_online
#> hamilton_online_parcels
#> - version: 0.11.0
#> - title: Hamilton Online Parcels
#> - homepage: <https://github.com/geomarker-io/parcel>
#> - description: A curated property-level data resource derived from scraping the
#> Hamilton County, OH Auditor Online: https://wedge1.hcauditor.org/. Data was
#> scraped for only residential parcels in CAGIS Parcels; see homepage for
#> details.
#> # A tibble: 259,653 × 7
#> parcel_id year_built n_total_rooms n_bedrooms n_full_bathrooms
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 6210023015500 1990 8 4 3
#> 2 6210023017000 1990 7 3 2
#> 3 6210023017700 1991 8 4 2
#> 4 6210023018100 1991 9 4 2
#> 5 6210023018500 1990 5 3 2
#> 6 6210023020300 1991 9 4 3
#> 7 6210023020500 1992 8 4 2
#> 8 6210023022800 1992 8 4 2
#> 9 6210023023100 1992 6 2 3
#> 10 6210023023300 1992 7 3 2
#> # ℹ 259,643 more rows
#> # ℹ 2 more variables: n_half_bathrooms <dbl>, online_market_total_value <dbl>
d_online@schema
#> parcel_id
#> - type: string
#> year_built
#> - type: number
#> n_total_rooms
#> - type: number
#> n_bedrooms
#> - type: number
#> n_full_bathrooms
#> - type: number
#> n_half_bathrooms
#> - type: number
#> online_market_total_value
#> - type: number
#> - description: May differ from the market_total_value from CAGIS auditor online
#> data. This value is scraped from the auditor's website.
# without fr:
# read.csv(fs::path_package("parcel", "hamilton_online_parcels"))Estimating the number of households per parcel
Certain calculations needs to be weighted by households instead of parcel; e.g. “What fraction of families live near roadway in Avondale?”. We assume the following as a conservative estimate of the number of households per parcel for each land_use code:
land_use |
n households |
|---|---|
| single family dwelling | 1 |
| condominium unit | 1 |
| two family dwelling | 2 |
| three family dwelling | 3 |
| apartment, 4-19 units | 4 |
| apartment, 20-39 units | 20 |
| apartment, 40+ units | 40 |
| landominium | 1 |
| charities, hospitals, retir | 1 |
| condo or pud garage | 1 |
| metropolitan housing authority | 1 |
| office / apartment over | 1 |
| manufactured home | 1 |
| other commercial housing | 1 |
| nursing home / private hospital | 1 |
| mobile home / trailer park | 1 |
| single fam dw 0-9 acr | 1 |
| independent living (seniors) | 1 |
| lihtc res | 1 |
| condominium office building | 0 |
| other residential structure | 0 |
| boataminium | 0 |
