Summary of available datasets
dataset_summary.RmdThe crimemappindata package contains 47 datasets that
are designed to be used in teaching crime mapping and crime analysis
more generally. There are several different types of data available:
- Point-level crime data.
- Area-level crime counts.
- Supporting data, such as population data and spatial boundaries.
The crimemappingdata package is licensed under the MIT
Licence, but the individual datasets have been licensed by the data
provider using a variety of different open-data licences. Check the
manual page for each dataset for details of the relevant licence. Users
are responsible for using the data in accordance with the applicable
licence for a dataset.
Point-level crime data
aggravated_assaults
Dataset of 8,696 aggravated assaults in Austin, TX, in 2019.
| date | longitude | latitude | location_type | location_category |
|---|---|---|---|---|
| 2019-01-01 00:00:00 | -97.71635 | 30.28631 | residence | residence |
| 2019-01-01 00:00:00 | -97.76135 | 30.24640 | residence | residence |
| 2019-01-01 00:01:00 | -97.73918 | 30.26679 | NA | NA |
| 2019-01-01 00:15:00 | -97.74007 | 30.26761 | NA | NA |
| 2019-01-01 00:27:00 | -97.83802 | 30.18048 | residence | residence |
| 2019-01-01 00:30:00 | -97.73909 | 30.26735 | NA | NA |
| 2019-01-01 00:51:00 | -97.69433 | 30.32696 | NA | NA |
| 2019-01-01 01:00:00 | -97.66867 | 30.43380 | residence | residence |
| 2019-01-01 01:00:00 | -97.70329 | 30.31954 | NA | NA |
| 2019-01-01 01:12:00 | -97.72503 | 30.32115 | residence | residence |
The stand-alone version of this dataset is different from the package
version. The stand-alone version is an Excel file containing three
sheets. Each sheet has aggravated-assault data for 2019 for a different
city: Austin, TX; Fort Worth, TX; and Seattle, WA. Students can get the
same data as is in the package version of the dataset by loading the
Austin sheet from the Excel file using the readxl package.
bronx_shootings
Dataset of 967 shootings in NYC in 2019 and separately of the 267 of those shootings that occurred in the Bronx.
| incident_key | boro | occur_date | murder | longitude | latitude |
|---|---|---|---|---|---|
| 191709964 | BROOKLYN | 2019-01-01 | TRUE | -73.86616 | 40.66568 |
| 191739125 | BROOKLYN | 2019-01-01 | FALSE | -73.97513 | 40.69514 |
| 191739126 | BRONX | 2019-01-01 | FALSE | -73.89581 | 40.85633 |
| 191790873 | BROOKLYN | 2019-01-02 | FALSE | -73.94445 | 40.69817 |
| 191851037 | BRONX | 2019-01-03 | FALSE | -73.85457 | 40.87122 |
| 191851038 | STATEN ISLAND | 2019-01-03 | FALSE | -74.16281 | 40.62472 |
| 191853461 | BROOKLYN | 2019-01-04 | FALSE | -73.99288 | 40.57376 |
| 191949899 | BROOKLYN | 2019-01-04 | FALSE | -73.94099 | 40.59836 |
| 191949900 | BRONX | 2019-01-06 | FALSE | -73.89287 | 40.82174 |
| 191949902 | BRONX | 2019-01-05 | FALSE | -73.85087 | 40.83122 |
The stand-alone version of these datasets are CSV files.
cdmx_car_jacking
Dataset of 2,811 car jacking offences in Mexico City in 2019.
| fecha_hechos | longitud | latitud | geom |
|---|---|---|---|
| 2019-05-17 17:00:00 | -99.10880 | 19.39638 | POINT (-99.1088 19.39638) |
| 2019-05-17 23:50:00 | -99.22413 | 19.29605 | POINT (-99.22413 19.29605) |
| 2019-05-09 22:00:00 | -99.19087 | 19.40981 | POINT (-99.19087 19.40981) |
| 2019-05-17 17:00:00 | -99.14227 | 19.45565 | POINT (-99.14227 19.45565) |
| 2019-05-17 21:30:00 | -99.04177 | 19.38836 | POINT (-99.04177 19.38836) |
| 2019-05-18 09:00:00 | -99.10008 | 19.32609 | POINT (-99.10008 19.32609) |
| 2019-05-20 08:30:00 | -99.10022 | 19.44002 | POINT (-99.10022 19.44002) |
| 2019-05-20 21:45:00 | -99.18412 | 19.49114 | POINT (-99.18412 19.49114) |
| 2019-05-20 23:00:00 | -99.16180 | 19.43733 | POINT (-99.1618 19.43733) |
| 2019-05-21 14:30:00 | -99.18892 | 19.38602 | POINT (-99.18892 19.38602) |
The stand-alone version of this dataset is a geopackage file.
chicago_aggravated_assaults
Dataset of 148,636 aggravated assaults in Chicago between 2010 and 2019.
| date | loc_cat | longitude | latitude | district |
|---|---|---|---|---|
| 2010-01-01 00:05:00 | residence | -87.6277 | 41.7922 | 2 |
| 2010-01-01 00:12:00 | street | -87.6683 | 41.7513 | 6 |
| 2010-01-01 00:30:00 | hotel | -87.6242 | 41.8727 | 1 |
| 2010-01-01 00:30:00 | street | -87.6478 | 41.7536 | 6 |
| 2010-01-01 00:54:00 | street | -87.6446 | 41.7720 | 7 |
| 2010-01-01 01:15:00 | street | -87.7311 | 41.8984 | 11 |
| 2010-01-01 01:45:00 | street | -87.6450 | 41.9228 | 18 |
| 2010-01-01 01:45:00 | street | -87.6106 | 41.7726 | 3 |
| 2010-01-01 01:48:00 | hotel | -87.6242 | 41.8727 | 1 |
| 2010-01-01 01:56:00 | residence | -87.6265 | 41.6916 | 5 |
The stand-alone version of this dataset is a gzipped CSV file.
cincinnati_burglary
Dataset of 8,723 burglaries in Cincinnati, Ohio, between 2016 and 2018.
| incident_no | offense_date | offense | longitude | latitude |
|---|---|---|---|---|
| 219027252 | 2017-05-25 00:00:00 | BURGLARY | -84.48973 | 39.14319 |
| 219027252 | 2017-05-25 00:00:00 | BURGLARY | -84.48881 | 39.14260 |
| 209003013 | 2018-10-19 12:00:00 | BURGLARY | -84.52658 | 39.12951 |
| 199004112 | 2018-02-27 16:10:00 | BURGLARY | -84.57675 | 39.19290 |
| 189027753 | 2018-10-10 11:00:00 | BURGLARY | -84.55841 | 39.12668 |
| 189027722 | 2018-10-12 08:00:00 | BURGLARY | -84.49692 | 39.10908 |
| 189027719 | 2018-10-15 12:45:31 | BURGLARY | -84.56619 | 39.10291 |
| 189026756 | 2018-10-04 02:45:00 | BURGLARY | -84.52020 | 39.14478 |
| 189026786 | 2018-10-04 03:00:00 | BURGLARY | -84.48522 | 39.12775 |
| 189026740 | 2018-10-03 19:10:00 | BURGLARY | -84.59645 | 39.15254 |
The stand-alone version of this dataset is a gzipped CSV file.
czechia_collisions and
czechia_mcycle_thefts
Datasets of 5,020 traffic collisions related to alcohol or drugs and separately of 9,464 thefts of two-wheeled motor vehicles in Czechia between 2020 and 2022.
| id | date | geom |
|---|---|---|
| 13115324 | 2020-01-01 00:20:00 | POINT (13.61072 50.13135) |
| 13115389 | 2020-01-01 00:50:00 | POINT (17.3977 50.12565) |
| 13115431 | 2020-01-01 00:59:00 | POINT (14.51836 48.91475) |
| 13115509 | 2020-01-01 02:00:00 | POINT (12.72899 49.85815) |
| 13115524 | 2020-01-01 02:01:00 | POINT (17.08994 49.47261) |
| 13115595 | 2020-01-01 03:09:00 | POINT (13.89387 50.15709) |
| 13115660 | 2020-01-01 02:45:00 | POINT (14.479 50.99283) |
| 13115688 | 2020-01-01 03:00:00 | POINT (17.04471 49.7781) |
| 13115696 | 2020-01-01 01:53:00 | POINT (17.11241 49.58678) |
| 13115701 | 2020-01-01 04:30:00 | POINT (13.79386 50.66278) |
| id | date | geom |
|---|---|---|
| 13115077 | 2020-01-01 02:00:00 | POINT (16.63238 49.18592) |
| 13115659 | 2020-01-01 04:21:00 | POINT (15.43107 50.68324) |
| 13115857 | 2020-01-01 10:45:00 | POINT (14.54159 50.0986) |
| 13117263 | 2020-01-01 22:58:00 | POINT (13.38625 49.72525) |
| 13117417 | 2020-01-02 04:28:00 | POINT (14.50531 50.12157) |
| 13117582 | 2020-01-02 08:24:00 | POINT (16.53362 49.26413) |
| 13117630 | 2020-01-02 08:29:00 | POINT (17.64119 49.22275) |
| 13118598 | 2020-01-02 04:30:00 | POINT (14.51453 50.13658) |
| 13119320 | 2020-01-03 09:24:00 | POINT (14.29876 50.06083) |
| 13119872 | 2020-01-02 14:35:00 | POINT (14.5243 50.09214) |
The stand-alone version of these datasets are geopackage files.
downtown_homicides and
glenrose_heights_homicides
Two datasets of homicides in the Downtown and Glenrose Heights
neighbourhoods of Atlanta, GA, in 2019. There are four rows in each
dataset. The label column contains a pre-formatted label
suitable for using in plotting the points on a map.
| report_number | label | longitude | latitude |
|---|---|---|---|
| 190191530 | 400 W PEACHTREE ST NW 19 January @ 15:00 |
-84.38760 | 33.76614 |
| 190570315 | 171 AUBURN AVE NE @CITY
WALK APARTMENTS 26 February @ 02:00 |
-84.38185 | 33.75546 |
| 192160018 | 241 FORSYTH ST SW 4 August @ 00:00 |
-84.39732 | 33.74827 |
| 193302338 | 80 JESSE HILL JR DR SE @GRADY<br>26 November @ 22:00 | -84.38198 | 33.75168 |
| report_number | label | longitude | latitude |
|---|---|---|---|
| 190540454 | 50 MT ZION RD SW 23 February @ 04:00 |
-84.38939 | 33.67086 |
| 190771445 | 3750 CROWN RD SW 18 March @ 16:00 |
-84.39397 | 33.65297 |
| 191672025 | 23 OAK DR SW 16 June @ 22:00 |
-84.39530 | 33.66918 |
| 193221226 | 2868 HAPEVILLE RD SW 18 November @ 13:00 |
-84.39311 | 33.67658 |
The stand-alone versions of these datasets are available as both CSV and geopackage files.
hungerford_shootings
Dataset of the locations of 16 shootings during the Hungerford massacre in 1987. This can be used for teaching mapping of crime series.
| victims | order | easting | northing |
|---|---|---|---|
| Susan GODFREY† | 1 | 423168 | 167694 |
| Kakaub DEAN | 2 | 429455 | 167904 |
| Roland MASON† |
Sheila MASON† Marjorie JACKSON Lisa MILDENHALL | 3| 433849| 168176| |Kenneth CLEMENTS† | 4| 434044| 168133| |Roger BRERETON† Linda CHAPMAN Alison CHAPMAN | 5| 433897| 168165| |Abdur KHAN† Alan LEPETIT Hazel HASLETT George WHITE† Dorothy RYAN† Ivor JACKSON | 6| 433821| 168176| |Betty TOLLADAY | 7| 433954| 168032| |Francis BUTLER† | 8| 433956| 167787| |Marcus BARNARD† | 9| 433872| 167765| |Ann HONEYBONE | 10| 433830| 167775|
The stand-alone version of this dataset is a CSV file.
lancashire_asb and northumbria_asb
Datasets of 56,434 anti-social behaviour incidents in Lancashire and 39,706 incidents in Northumbria, England, in 2022.
| month | longitude | latitude | location | lsoa_code |
|---|---|---|---|---|
| 2022-01-01 | -2.4646 | 53.7655 | Hazelwood Close | E01012607 |
| 2022-01-01 | -2.4747 | 53.7702 | Peridot Close | E01012608 |
| 2022-01-01 | -2.4747 | 53.7702 | Peridot Close | E01012608 |
| 2022-01-01 | -2.4762 | 53.7684 | Jasper Street | E01012608 |
| 2022-01-01 | -2.4668 | 53.7641 | Lilac Road | E01012608 |
| 2022-01-01 | -2.4668 | 53.7641 | Lilac Road | E01012608 |
| 2022-01-01 | -2.4747 | 53.7702 | Peridot Close | E01012608 |
| 2022-01-01 | -2.4785 | 53.7706 | Ruby Street | E01012633 |
| 2022-01-01 | -2.4796 | 53.7677 | Pemberton Street | E01012634 |
| 2022-01-01 | -2.4793 | 53.7652 | Cranshaw Drive | E01012634 |
| month | longitude | latitude | location | lsoa_code |
|---|---|---|---|---|
| 2022-01-01 | -2.0020 | 55.7663 | Palace Green | E01027377 |
| 2022-01-01 | -2.0016 | 55.7712 | Parade | E01027377 |
| 2022-01-01 | -2.0002 | 55.7675 | Ness Street | E01027377 |
| 2022-01-01 | -2.0030 | 55.7668 | Palace Street | E01027377 |
| 2022-01-01 | -2.0103 | 55.7586 | Shopping Area | E01027385 |
| 2022-01-01 | -2.0134 | 55.7566 | Grove Gardens South | E01027385 |
| 2022-01-01 | -2.0085 | 55.7581 | Northumberland Road | E01027385 |
| 2022-01-01 | -2.0103 | 55.7586 | Shopping Area | E01027385 |
| 2022-01-01 | -2.0012 | 55.7566 | Adams Drive | E01027388 |
| 2022-01-01 | -2.0055 | 55.7582 | Billendean Terrace | E01027388 |
The stand-alone version of these datasets are tab-separated values
(.tab) files. The data in these file can be used together
with the
lancashire_districts/northumbria_districts,
lancashire_wards/northumbria_wards and
lancashire_ward_pop/northumbria_ward_pop
datasets to calculate area-level incidence rates.
london_attacks
Dataset of 8 fatal terrorist attacks in London from 2010 to 2018.
| date | latitude | longitude | description |
|---|---|---|---|
| 2005-07-07 | 51.5186 | -0.0813 | Suicide bomb exploded on train, killing 7 |
| 2005-07-07 | 51.5200 | -0.1678 | Suicide bomb exploded on train, killing 6 |
| 2005-07-07 | 51.5302 | -0.1241 | Suicide bomb exploded on train, killing 26 |
| 2005-07-07 | 51.5250 | -0.1291 | Suicide bomb exploded on bus, killing 13 |
| 2013-05-22 | 51.4883 | 0.0623 | Soldier fatally stabbed |
| 2017-03-22 | 51.5008 | -0.1219 | Car driven at pedestrians and people stabbed, killing 5 |
The stand-alone version of this dataset is a CSV file.
london_crimes
Dataset of 849,409 crimes recorded in London in 2022.
| month | type | location_type | longitude | latitude |
|---|---|---|---|---|
| 1 | criminal damage or arson | NA | -0.1349 | 51.5067 |
| 1 | violent or sexual | NA | -0.3751 | 51.4365 |
| 1 | public order | Rail Station | 0.0812 | 51.5398 |
| 1 | violent or sexual | NA | 0.0804 | 51.4840 |
| 1 | violent or sexual | NA | -0.3513 | 51.5078 |
| 1 | other theft | Hospital | -0.0170 | 51.4536 |
| 1 | criminal damage or arson | NA | -0.1497 | 51.4908 |
| 1 | violent or sexual | NA | -0.2481 | 51.4637 |
| 1 | violent or sexual | Rail Station | -0.1372 | 51.3200 |
| 1 | robbery | NA | -0.1254 | 51.5197 |
The stand-alone version of this dataset is a gzipped CSV file.
medellin_homicides
Dataset of 9,360 homicides in Medellin, Colombia, from 2010 to 2019.
| fecha_hecho | longitud | latitud | sexo | edad | modalidad |
|---|---|---|---|---|---|
| 2019-04-23 12:30:00 | -75.56985 | 6.257226 | Mujer | 50 | Ahorcamiento o estrangulamiento |
| 2019-05-12 00:51:00 | -75.61037 | 6.222806 | Hombre | 24 | Arma de fuego |
| 2019-05-12 02:36:00 | -75.62039 | 6.262382 | Hombre | 34 | Arma de fuego |
| 2019-05-12 03:03:00 | -75.56177 | 6.269589 | Hombre | 20 | Arma de fuego |
| 2019-05-12 22:40:00 | -75.53269 | 6.236277 | Hombre | 34 | Arma de fuego |
| 2019-05-12 19:30:00 | -75.64196 | 6.196195 | Hombre | 25 | Arma de fuego |
| 2019-05-12 04:00:00 | -75.55532 | 6.290154 | Hombre | 37 | Cortopunzante |
| 2019-05-13 05:34:00 | -75.55720 | 6.271623 | Hombre | 22 | Arma de fuego |
| 2019-05-12 04:50:00 | -75.56665 | 6.240023 | Mujer | 21 | Ahorcamiento o estrangulamiento |
| 2019-05-14 01:00:00 | -75.56220 | 6.249262 | Hombre | 29 | Arma de fuego |
The stand-alone version of this dataset is a CSV file. Note that the CSV file uses semi colons as column delimiters and commas as decimal separators in numbers, in accordance with Colombian practice. This file can be read with functions such as [readr::read_csv2()].
This dataset can be used together with the
medellin_metro_lines and medellin_metro_stns
datasets to teach topics such as finding or counting crimes within a
certain distance of a facility (e.g. a metro station).
nottingham_burglary and
nottingham_robbery
Datasets of 1,795 burglaries and 555 robberies in Nottingham, England, in 2022.
| month | longitude | latitude | location | lsoa_code |
|---|---|---|---|---|
| 2022-01-01 | -1.1704 | 53.0050 | Hathersage Close | E01013893 |
| 2022-01-01 | -1.1916 | 53.0140 | Fradley Close | E01013879 |
| 2022-01-01 | -1.2076 | 53.0047 | Milford Close | E01013880 |
| 2022-01-01 | -1.2050 | 53.0046 | Aldgate Close | E01013880 |
| 2022-01-01 | -1.2076 | 53.0047 | Milford Close | E01013880 |
| 2022-01-01 | -1.1987 | 53.0057 | Ravensworth Road | E01013882 |
| 2022-01-01 | -1.1955 | 53.0023 | Montague Street | E01013885 |
| 2022-01-01 | -1.1789 | 52.9959 | Kersall Drive | E01013889 |
| 2022-01-01 | -1.1810 | 52.9963 | St Albans Road | E01013889 |
| 2022-01-01 | -1.1946 | 52.9976 | Latimer Close | E01013891 |
| month | longitude | latitude | location | lsoa_code |
|---|---|---|---|---|
| 2022-01-01 | -1.0943 | 52.9550 | Daleside Road East | E01028184 |
| 2022-01-01 | -1.1968 | 52.9999 | Main Street | E01013885 |
| 2022-01-01 | -1.1789 | 52.9959 | Kersall Drive | E01013889 |
| 2022-01-01 | -1.1789 | 52.9959 | Kersall Drive | E01013889 |
| 2022-01-01 | -1.1547 | 52.9951 | Babbacombe Drive | E01013853 |
| 2022-01-01 | -1.1870 | 52.9888 | Ryder Street | E01013829 |
| 2022-01-01 | -1.1942 | 52.9831 | Sports/Recreation Area | E01013830 |
| 2022-01-01 | -1.1954 | 52.9821 | Nuthall Road | E01013819 |
| 2022-01-01 | -1.2018 | 52.9788 | Winsford Close | E01013820 |
| 2022-01-01 | -1.1520 | 52.9682 | Thorncliffe Road | E01013941 |
The stand-alone versions of these datasets are gzipped CSV files. The
data in these files can be used together with the
nottingham_wards dataset teach spatial clipping, counting
incidents in areas and related topics.
nyc_shootings
Dataset of 967 shootings in New York City recorded by the New York City Police Department (NYPD) in 2019.
| incident_key | boro | occur_date | murder | longitude | latitude |
|---|---|---|---|---|---|
| 191709964 | BROOKLYN | 2019-01-01 | TRUE | -73.86616 | 40.66568 |
| 191739125 | BROOKLYN | 2019-01-01 | FALSE | -73.97513 | 40.69514 |
| 191739126 | BRONX | 2019-01-01 | FALSE | -73.89581 | 40.85633 |
| 191790873 | BROOKLYN | 2019-01-02 | FALSE | -73.94445 | 40.69817 |
| 191851037 | BRONX | 2019-01-03 | FALSE | -73.85457 | 40.87122 |
| 191851038 | STATEN ISLAND | 2019-01-03 | FALSE | -74.16281 | 40.62472 |
| 191853461 | BROOKLYN | 2019-01-04 | FALSE | -73.99288 | 40.57376 |
| 191949899 | BROOKLYN | 2019-01-04 | FALSE | -73.94099 | 40.59836 |
| 191949900 | BRONX | 2019-01-06 | FALSE | -73.89287 | 40.82174 |
| 191949902 | BRONX | 2019-01-05 | FALSE | -73.85087 | 40.83122 |
The stand-alone version of this dataset is a CSV file.
This dataset can be used together with the nyc_precincts
dataset to teach topics such as counting the number of crimes in
particular areas.
san_francisco_robbery
Dataset of 951 personal robberies in San Francisco in 2019.
| uid | offense_type | date_time | longitude | latitude |
|---|---|---|---|---|
| 24103841 | personal robbery | 2019-01-01 19:50:00 | -122.4021 | 37.78872 |
| 24103948 | personal robbery | 2019-01-02 08:00:00 | -122.4085 | 37.78445 |
| 24104162 | personal robbery | 2019-01-03 00:30:00 | -122.3900 | 37.75527 |
| 24104203 | personal robbery | 2019-01-03 03:13:00 | -122.4210 | 37.78382 |
| 24104237 | personal robbery | 2019-01-03 09:30:00 | -122.4723 | 37.72162 |
| 24104238 | personal robbery | 2019-01-03 09:30:00 | -122.4723 | 37.72162 |
| 24104239 | personal robbery | 2019-01-03 09:30:00 | -122.4723 | 37.72162 |
| 24104752 | personal robbery | 2019-01-05 02:23:00 | -122.4138 | 37.77452 |
| 24105017 | personal robbery | 2019-01-06 01:00:00 | -122.3979 | 37.78704 |
| 24105062 | personal robbery | 2019-01-06 10:22:00 | -122.4073 | 37.78456 |
The stand-alone version of this dataset is a CSV file.
sao_paulo_homicides
Dataset of 6,214 homicides recorded by police in the immediate region of Sao Paulo, Brazil, between 2017 and 2022.
| year | date | municipality | location_type | victim_sex | longitude | latitude |
|---|---|---|---|---|---|---|
| 2017 | 2017-01-01 | São Paulo | Via pública | Masculino | -46.75288 | -23.42660 |
| 2017 | 2017-01-01 | São Paulo | Via pública | Masculino | -46.69175 | -23.46374 |
| 2017 | 2017-01-01 | São Paulo | Via pública | Masculino | -46.72815 | -23.44609 |
| 2017 | 2017-01-01 | Diadema | Via pública | Masculino | -46.59971 | -23.70628 |
| 2017 | 2017-01-01 | Guarulhos | Restaurante e afins | Masculino | -46.49450 | -23.42217 |
| 2017 | 2017-01-01 | Mauá | Via pública | Masculino | -46.44718 | -23.65284 |
| 2017 | 2017-01-01 | São Bernardo do Campo | Rodovia/Estrada | Masculino | -46.57781 | -23.83839 |
| 2017 | 2017-01-02 | São Paulo | Via pública | Masculino | -46.79244 | -23.68158 |
| 2017 | 2017-01-02 | São Paulo | Area não ocupada | Indefinido | -46.69767 | -23.76990 |
| 2017 | 2017-01-02 | Santa Isabel | Area não ocupada | Masculino | -46.22515 | -23.32094 |
The stand-alone version of this dataset is an Excel
(.xlsx) file.
tempe_opioid_calls
Dataset of 3,379 calls to emergency services related to opioid overdoses in Tempe, Arizona, from 2017 to 2024.
| year | incident_date | narcan_given | latitude | longitude |
|---|---|---|---|---|
| 2017 | 2017-01-03 | NA | 33.4196 | -111.8904 |
| 2017 | 2017-01-09 | NA | 33.3971 | -111.9249 |
| 2017 | 2017-01-09 | NA | 33.4297 | -111.9334 |
| 2017 | 2017-01-09 | NA | 33.3681 | -111.9014 |
| 2017 | 2017-01-12 | NA | 33.4647 | -111.9212 |
| 2017 | 2017-01-12 | NA | 33.4180 | -111.8837 |
| 2017 | 2017-01-14 | NA | 33.4210 | -111.9038 |
| 2017 | 2017-01-15 | NA | 33.3591 | -111.9050 |
| 2017 | 2017-01-18 | NA | 33.3528 | -111.9420 |
| 2017 | 2017-01-18 | NA | 33.3656 | -111.8734 |
The stand-alone version of this dataset is a CSV file.
vancouver_thefts
Dataset of 21,918 thefts in the City of Vancouver in 2020.
| TYPE | YEAR | MONTH | DAY | HOUR | MINUTE | HUNDRED_BLOCK | NEIGHBOURHOOD | X | Y |
|---|---|---|---|---|---|---|---|---|---|
| Other Theft | 2020 | 1 | 1 | 0 | 0 | 11XX BURNABY ST | West End | 490320.3 | 5458607 |
| Other Theft | 2020 | 1 | 1 | 0 | 0 | 13XX W 71ST AVE | Marpole | 489998.2 | 5450474 |
| Other Theft | 2020 | 1 | 1 | 0 | 1 | 18XX E GEORGIA ST | Grandview-Woodland | 495141.1 | 5458406 |
| Other Theft | 2020 | 1 | 1 | 0 | 0 | 2X ALEXANDER ST | Central Business District | 492471.3 | 5458985 |
| Theft from Vehicle | 2020 | 1 | 1 | 0 | 0 | 11XX SKEENA ST | Hastings-Sunrise | 497938.7 | 5457949 |
| Theft from Vehicle | 2020 | 1 | 1 | 0 | 10 | 14XX LABURNUM ST | Kitsilano | 488991.7 | 5457823 |
| Theft from Vehicle | 2020 | 1 | 1 | 0 | 30 | 14XX W 11TH AVE | Fairview | 490013.1 | 5456560 |
| Theft from Vehicle | 2020 | 1 | 1 | 0 | 0 | 33XX OAK ST | South Cambie | 490744.3 | 5455927 |
| Theft from Vehicle | 2020 | 1 | 1 | 0 | 30 | 42XX SKEENA ST | Renfrew-Collingwood | 497927.5 | 5454905 |
| Theft from Vehicle | 2020 | 1 | 1 | 0 | 0 | 45XX CLANCY LORANGER WAY | Riley Park | 492098.3 | 5454511 |
The stand-alone version of this dataset is a gzipped CSV file.
Area-level crime data
germany_violence_counts and
japan_violence_counts
Datasets of annual counts of different types of violent crime in states of Germany in 2019 and prefectures of Japan in 2012, together with population data and GDP per capita data.
| state | measure | count |
|---|---|---|
| Baden-Württemberg | GDP per capita (€1000) | 45.108 |
| Baden-Württemberg | number of homicides | 379.000 |
| Baden-Württemberg | number of rapes | 820.000 |
| Baden-Württemberg | number of robberies | 2868.000 |
| Baden-Württemberg | population | 11103043.000 |
| Bayern | GDP per capita (€1000) | 46.498 |
| Bayern | number of homicides | 529.000 |
| Bayern | number of rapes | 1067.000 |
| Bayern | number of robberies | 2083.000 |
| Bayern | population | 13140183.000 |
| prefecture | measure | value |
|---|---|---|
| Hokkaido | number of homicides | 55.00 |
| Hokkaido | number of robberies | 121.00 |
| Hokkaido | number of rapes | 48.00 |
| Hokkaido | number of violent crimes | 2188.00 |
| Hokkaido | GDP per capita (¥1000) | 3712.99 |
| Hokkaido | population (thousands) | 5246.00 |
| Aomori | number of homicides | 8.00 |
| Aomori | number of robberies | 6.00 |
| Aomori | number of rapes | 12.00 |
| Aomori | number of violent crimes | 602.00 |
The format of these datasets is designed to be challenging for students, who will need to reformat the data and wrangle it to create population counts. This is designed to reflect the formats of data that are often provided by statistical agencies.
The stand-alone versions of these datasets are .rds
files.
malaysia_violence_counts
Dataset of annual counts of five types of violent crime in states of Malaysia from 2006 to 2017.
| region | state | year | crime_type | count |
|---|---|---|---|---|
| East Malaysia | Sabah | 2006 | aggravated assault | 301 |
| East Malaysia | Sabah | 2006 | armed robbery | 8 |
| East Malaysia | Sabah | 2006 | murder | 83 |
| East Malaysia | Sabah | 2006 | rape | 200 |
| East Malaysia | Sabah | 2006 | unarmed robbery | 563 |
| East Malaysia | Sabah | 2007 | aggravated assault | 389 |
| East Malaysia | Sabah | 2007 | armed robbery | 1 |
| East Malaysia | Sabah | 2007 | murder | 68 |
| East Malaysia | Sabah | 2007 | rape | 204 |
| East Malaysia | Sabah | 2007 | unarmed robbery | 641 |
The stand-alone version of this dataset is a .rds
file.
northants_burglary_counts
Dataset of counts of burglaries in 409 lower-layer super output areas in Northamptonshire, England, in 2020.
| district | lsoa | count |
|---|---|---|
| Corby | Corby 001A | 6 |
| Corby | Corby 001B | 7 |
| Corby | Corby 001C | 5 |
| Corby | Corby 001D | 29 |
| Corby | Corby 002A | 9 |
| Corby | Corby 002B | 3 |
| Corby | Corby 002C | 4 |
| Corby | Corby 002D | 4 |
| Corby | Corby 002E | 4 |
| Corby | Corby 002F | 4 |
The stand-alone version of this dataset is a .rds
file.
qld_stalking
Dataset of 7,304 annual counts of stalking offences recorded by the Queensland Police Service in police divisions from 2001 to 2022.
| division | year | stalking |
|---|---|---|
| Acacia Ridge | 2001 | 6 |
| Acacia Ridge | 2002 | 7 |
| Acacia Ridge | 2003 | 13 |
| Acacia Ridge | 2004 | 1 |
| Acacia Ridge | 2005 | 6 |
| Acacia Ridge | 2006 | 9 |
| Acacia Ridge | 2007 | 12 |
| Acacia Ridge | 2008 | 0 |
| Acacia Ridge | 2009 | 3 |
| Acacia Ridge | 2010 | 4 |
The stand-alone version of this dataset is an Excel
(.xlsx) file.
uttar_pradesh_murders
Dataset of counts of murders recorded by police in the 75 administrative districts of the state of Uttar Pradesh, India, in 2014.
| district | murder |
|---|---|
| Agra | 178 |
| Aligarh | 179 |
| Allahabad | 132 |
| Ambedkar Nagar | 24 |
| Amethi | 36 |
| Amroha | 60 |
| Auraiya | 34 |
| Azamgarh | 68 |
| Budaun | 120 |
| Baghpat | 83 |
The stand-alone version of this dataset is a CSV file.
Supporting data
cdmx_alcaldias
Simple features (SF) object containing the boundaries of the alcaldías (boroughs) of Mexico City.
| municip | nomgeo | geometry |
|---|---|---|
| 9 | Milpa Alta | <SF polygon> |
| 14 | Benito Juárez | <SF polygon> |
| 5 | Gustavo A. Madero | <SF polygon> |
| 3 | Coyoacán | <SF polygon> |
| 16 | Miguel Hidalgo | <SF polygon> |
| 8 | La Magdalena Contreras | <SF polygon> |
| 11 | Tláhuac | <SF polygon> |
| 2 | Azcapotzalco | <SF polygon> |
| 6 | Iztacalco | <SF polygon> |
| 10 | Álvaro Obregón | <SF polygon> |
The stand-alone version of this dataset is a geopackage file.
chicago_police_districts
Simple features (SF) object containing the boundaries of Chicago Police Department districts.
| Name | Description | geometry |
|---|---|---|
| 17 | District 17 | <SF polygon> |
| 20 | District 20 | <SF polygon> |
| 31 | District 31 | <SF polygon> |
| 31 | District 31 | <SF polygon> |
| 19 | District 19 | <SF polygon> |
| 25 | District 25 | <SF polygon> |
| 14 | District 14 | <SF polygon> |
| 31 | District 31 | <SF polygon> |
| 22 | District 22 | <SF polygon> |
| 5 | District 5 | <SF polygon> |
The stand-alone version of this dataset is a geopackage file.
japan_prefectures
Simple Features (SF) object containing the boundaries of the prefectures of Japan.
| adm0_en | adm0_ja | adm0_pcode | adm1_en | adm1_ja | adm1_pcode | geometry |
|---|---|---|---|---|---|---|
| Japan | 日本 | JP | Aichi | 愛知県 | JP23 | <SF polygon> |
| Japan | 日本 | JP | Akita | 秋田県 | JP05 | <SF polygon> |
| Japan | 日本 | JP | Aomori | 青森県 | JP02 | <SF polygon> |
| Japan | 日本 | JP | Chiba | 千葉県 | JP12 | <SF polygon> |
| Japan | 日本 | JP | Ehime | 愛媛県 | JP38 | <SF polygon> |
| Japan | 日本 | JP | Fukui | 福井県 | JP18 | <SF polygon> |
| Japan | 日本 | JP | Fukuoka | 福岡県 | JP40 | <SF polygon> |
| Japan | 日本 | JP | Fukushima | 福島県 | JP07 | <SF polygon> |
| Japan | 日本 | JP | Gifu | 岐阜県 | JP21 | <SF polygon> |
| Japan | 日本 | JP | Gunma | 群馬県 | JP10 | <SF polygon> |
The stand-alone version of this dataset is a geopackage file.
lancashire_districts, lancashire_wards,
northumbria_districts and
northumbria_wards
Simple features (SF) objects containing the boundaries of local government wards and districts in Lancashire and Northumbria, England. Boundaries are for 2022.
| objectid | district | geometry |
|---|---|---|
| E06000008 | Blackburn with Darwen | <SF polygon> |
| E06000009 | Blackpool | <SF polygon> |
| E07000117 | Burnley | <SF polygon> |
| E07000118 | Chorley | <SF polygon> |
| E07000119 | Fylde | <SF polygon> |
| E07000120 | Hyndburn | <SF polygon> |
| E07000121 | Lancaster | <SF polygon> |
| E07000122 | Pendle | <SF polygon> |
| E07000123 | Preston | <SF polygon> |
| E07000124 | Ribble Valley | <SF polygon> |
| ward_code | ward_name | geometry |
|---|---|---|
| E05001643 | Anchorsholme | <SF polygon> |
| E05001644 | Bispham | <SF polygon> |
| E05001645 | Bloomfield | <SF polygon> |
| E05001646 | Brunswick | <SF polygon> |
| E05001647 | Claremont | <SF polygon> |
| E05001648 | Clifton | <SF polygon> |
| E05001649 | Greenlands | <SF polygon> |
| E05001650 | Hawes Side | <SF polygon> |
| E05001651 | Highfield | <SF polygon> |
| E05001652 | Ingthorpe | <SF polygon> |
| objectid | district | geometry |
|---|---|---|
| E06000057 | Northumberland | <SF polygon> |
| E08000021 | Newcastle upon Tyne | <SF polygon> |
| E08000022 | North Tyneside | <SF polygon> |
| E08000023 | South Tyneside | <SF polygon> |
| E08000024 | Sunderland | <SF polygon> |
| E08000037 | Gateshead | <SF polygon> |
| ward_code | ward_name | geometry |
|---|---|---|
| E05001067 | Birtley | <SF polygon> |
| E05001068 | Blaydon | <SF polygon> |
| E05001069 | Bridges | <SF polygon> |
| E05001071 | Chowdene | <SF polygon> |
| E05001072 | Crawcrook and Greenside | <SF polygon> |
| E05001073 | Deckham | <SF polygon> |
| E05001074 | Dunston and Teams | <SF polygon> |
| E05001075 | Dunston Hill and Whickham East | <SF polygon> |
| E05001076 | Felling | <SF polygon> |
| E05001077 | High Fell | <SF polygon> |
The stand-alone versions of
lancashire_districts/northumbria_districts are
in geoJSON format and the stand-alone versions of
lancashire_wards/northumbria_wards are
geopackage files.
lancashire_ward_pop and
northumbria_ward_pop
Datasets of usual resident population in 2020 in each local
government ward in Lancashire and Northumbria, England. These can be
used together with the
lancashire_asb/northumbria_asb and
lancashire_wards/northumbria_wards datasets
for teaching non-spatial joining and calculation of area crime
rates.
| gss_code | ward | population |
|---|---|---|
| E05001643 | Anchorsholme | 6278 |
| E05001644 | Bispham | 6291 |
| E05001645 | Bloomfield | 7345 |
| E05001646 | Brunswick | 6890 |
| E05001647 | Claremont | 7178 |
| E05001648 | Clifton | 6589 |
| E05001649 | Greenlands | 6714 |
| E05001650 | Hawes Side | 7010 |
| E05001651 | Highfield | 6367 |
| E05001652 | Ingthorpe | 6468 |
| gss_code | ward | population |
|---|---|---|
| E05001067 | Birtley | 8028 |
| E05001068 | Blaydon | 9970 |
| E05001069 | Bridges | 11515 |
| E05001071 | Chowdene | 8647 |
| E05001072 | Crawcrook and Greenside | 8786 |
| E05001073 | Deckham | 9786 |
| E05001074 | Dunston and Teams | 9359 |
| E05001075 | Dunston Hill and Whickham East | 9005 |
| E05001076 | Felling | 9104 |
| E05001077 | High Fell | 9529 |
The stand-alone versions of these dataset are Excel
(.xlsx) files.
medellin_metro_lines and
medellin_metro_stns
Simple features (SF) objects containing the locations of metro lines and stations in Medellin, Colombia.
#> Warning in medellin_metro_lines_head$geometry <- "<SF polyline>":
#> Coercing LHS to a list
|
|
| linea | nombre | municipio | sistema | x | y |
|---|---|---|---|---|---|
| A | Estación Acevedo (Línea A) | Medellín | Metro | -75.55856 | 6.299943 |
| A | Estación Tricentenario (Línea A) | Medellín | Metro | -75.56472 | 6.290315 |
| A | Estación Caribe (Línea A) | Medellín | Metro | -75.56940 | 6.278327 |
| A | Estación Universidad (Línea A) | Medellín | Metro | -75.56583 | 6.269433 |
| A | Estación Hospital (Línea A) | Medellín | Metro | -75.56333 | 6.263920 |
| A | Estación Prado (Línea A) | Medellín | Metro | -75.56617 | 6.256850 |
| A | Estación Parque Berrío (Línea A) | Medellín | Metro | -75.56824 | 6.250502 |
| A | Estación San Antonio (Línea A) | Medellín | Metro | -75.56970 | 6.247194 |
| B | Estación Cisneros (Línea B) | Medellín | Metro | -75.57515 | 6.249054 |
| A | Estación Alpujarra (Línea A) | Medellín | Metro | -75.57143 | 6.242917 |
The stand-alone version of the medellin_metro_lines
dataset is a zipped shapefile. The medellin_metro_stn
dataset is a CSV file.
These datasets can be used together with the
medellin_homicides dataset to teach topics such as finding
or counting crimes within a certain distance of a facility (e.g. a metro
station).
nottingham_wards
Simple features (SF) objects containing the boundaries of local government wards and districts in Nottingham, England. Boundaries are for 2022.
| ward_code | ward_name | geometry |
|---|---|---|
| E05012270 | Aspley | <SF polygon> |
| E05012271 | Basford | <SF polygon> |
| E05012272 | Berridge | <SF polygon> |
| E05012273 | Bestwood | <SF polygon> |
| E05012274 | Bilborough | <SF polygon> |
| E05012275 | Bulwell | <SF polygon> |
| E05012276 | Bulwell Forest | <SF polygon> |
| E05012277 | Castle | <SF polygon> |
| E05012278 | Clifton East | <SF polygon> |
| E05012279 | Clifton West | <SF polygon> |
The stand-alone version of this file is in geopackage format. The
data in these files can be used together with the
nottingham_wards dataset teach spatial clipping, counting
incidents in areas and related topics.
nyc_precincts
Dataset of the 77 police precincts in New York City.
| OBJECTID | Precinct | geom |
|---|---|---|
| 1 | 1 | MULTIPOLYGON (((-74.04388 4… |
| 2 | 5 | MULTIPOLYGON (((-73.98864 4… |
| 3 | 6 | MULTIPOLYGON (((-73.99969 4… |
| 4 | 7 | MULTIPOLYGON (((-73.97346 4… |
| 5 | 9 | MULTIPOLYGON (((-73.97161 4… |
| 6 | 10 | MULTIPOLYGON (((-74.00139 4… |
| 7 | 13 | MULTIPOLYGON (((-73.98156 4… |
| 8 | 14 | MULTIPOLYGON (((-73.97465 4… |
| 9 | 17 | MULTIPOLYGON (((-73.95904 4… |
| 10 | 18 | MULTIPOLYGON (((-73.99394 4… |
The stand-alone version of this dataset is a geopackage file.
This dataset can be used together with the nyc_shootings
dataset to teach topics such as counting the number of crimes in
particular areas.
qld_police_divisions
Simple features (SF) object containing the boundaries of divisions of the Queensland Police Service as of 2022.
| division | geometry |
|---|---|
| Acacia Ridge | <SF polygon> |
| Adavale | <SF polygon> |
| Agnes Water | <SF polygon> |
| Albany Creek | <SF polygon> |
| Allora | <SF polygon> |
| Alpha | <SF polygon> |
| Anakie | <SF polygon> |
| Annerley | <SF polygon> |
| Aramac | <SF polygon> |
| Atherton | <SF polygon> |
The stand-alone version of this dataset is a geopackage file.
qld_population
Dataset of usual resident population in 2016 in each Queensland
Police Service division area. This can be used together with the
qld_police_divisions and qld_stalking
datasets, especially for teaching non-spatial joining and calculation of
area crime rates.
| police_division | population |
|---|---|
| Acacia Ridge | 40123 |
| Adavale | 101 |
| Agnes Water | 2674 |
| Albany Creek | 49336 |
| Allora | 3003 |
| Alpha | 510 |
| Anakie | 1599 |
| Annerley | 25377 |
| Aramac | 441 |
| Atherton | 12092 |
The stand-alone version of this dataset is gzipped CSV file.
sao_paulo_muni
Simple features (SF) object containing boundaries of municipality boundaries in the immediate region of Sao Paulo, Brazil, 2010.
| code_muni | name_muni | geometry | |
|---|---|---|---|
| 31 | 3502754 | Araçariguama | <SF polygon> |
| 45 | 3503901 | Arujá | <SF polygon> |
| 48 | 3504107 | Atibaia | <SF polygon> |
| 65 | 3505708 | Barueri | <SF polygon> |
| 72 | 3506359 | Bertioga | <SF polygon> |
| 75 | 3506607 | Biritiba-Mirim | <SF polygon> |
| 96 | 3508405 | Cabreúva | <SF polygon> |
| 102 | 3509007 | Caieiras | <SF polygon> |
| 104 | 3509205 | Cajamar | <SF polygon> |
| 110 | 3509601 | Campo Limpo Paulista | <SF polygon> |
The stand-alone version of this dataset is a geoJSON file.
uttar_pradesh_districts
Simple features (SF) object containing boundaries of administrative districts in Uttar Pradesh, India.
| state | district_name | geometry |
|---|---|---|
| Uttar Pradesh | Agra | <SF polygon> |
| Uttar Pradesh | Bareilly | <SF polygon> |
| Uttar Pradesh | Etah | <SF polygon> |
| Uttar Pradesh | Shahjahanpur | <SF polygon> |
| Uttar Pradesh | Pilibhit | <SF polygon> |
| Uttar Pradesh | Allahabad | <SF polygon> |
| Uttar Pradesh | Pratapgarh | <SF polygon> |
| Uttar Pradesh | Jhansi | <SF polygon> |
| Uttar Pradesh | Jalaun | <SF polygon> |
| Uttar Pradesh | Jaunpur | <SF polygon> |
The stand-alone version of this dataset is a geopackage file.
uttar_pradesh_population
Dataset of usual resident population in 2011 in each administrative
district in Uttar Pradesh. This can be used together with the
uttar_pradesh_districts and
uttar_pradesh_murders datasets, especially for teaching
non-spatial joining and calculation of area crime rates.
| code | district | headquarters | division | population | area | density_km2 |
|---|---|---|---|---|---|---|
| AG | Agra | Agra | Agra | 4418797 | 4041 | 1093 |
| AL | Aligarh | Aligarh | Aligarh | 3673889 | 3788 | 1007 |
| AH | Allahabad | Allahabad | Allahabad | 5954391 | 5482 | 1086 |
| AN | Ambedkar Nagar | Akbarpur | Faizabad | 2397888 | 2350 | 1020 |
| AM | Amethi | Gauriganj | Faizabad | 2050133 | 2651 | 773 |
| JP | Amroha | Amroha | Moradabad | 1840221 | 2249 | 818 |
| AU | Auraiya | Auraiya | Kanpur | 1379545 | 2016 | 684 |
| AZ | Azamgarh | Azamgarh | Azamgarh | 4613913 | 4054 | 1138 |
| BD | Budaun | Badaun | Bareilly | 3127621 | 4234 | 2368 |
| BG | Baghpat | Baghpat | Meerut | 1303048 | 5237 | 249 |
The stand-alone version of this dataset is a CSV file.