Files | Size | Format | Created | Updated | License | Source |
---|---|---|---|---|---|---|
3 | 772kB | csv zip | 4 years ago | 4 years ago | Open Government Licence |
Download files in this dataset
File | Description | Size | Last changed | Download |
---|---|---|---|---|
crime-rates | 45kB | csv (45kB) , json (451kB) | ||
recorded-offences | 25kB | csv (25kB) , json (57kB) | ||
crime_zip | Compressed versions of dataset. Includes normalized CSV and JSON data with original data and datapackage.json. | 50kB | zip (50kB) |
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
Code | 1 | any (default) | |
Borough | 2 | string (default) | |
Mid-year estimates 1999 | 3 | any (default) | |
Mid-year estimates 2000 | 4 | any (default) | |
Mid-year estimates 2001 | 5 | any (default) | |
Mid-year estimates 2002 | 6 | any (default) | |
Mid-year estimates 2003 | 7 | any (default) | |
Mid-year estimates 2004 | 8 | any (default) | |
Mid-year estimates 2005 | 9 | any (default) | |
Mid-year estimates 2006 | 10 | any (default) | |
Mid-year estimates 2007 | 11 | any (default) | |
Mid-year estimates 2008 | 12 | any (default) | |
Mid-year estimates 2009 | 13 | any (default) | |
Mid-year estimates 2010 | 14 | any (default) | |
Mid-year estimates 2011 | 15 | any (default) | |
Mid-year estimates 2012 | 16 | any (default) | |
Mid-year estimates 2013 | 17 | any (default) | |
Mid-year estimates 2014 | 18 | any (default) | |
Mid-year estimates 2015 | 19 | any (default) | |
Mid-year estimates 2016 | 20 | any (default) | |
Year | 21 | date (%Y-%m-%d) | |
Value | 22 | any (default) |
This is a preview version. There might be more data in the original version.
Field Name | Order | Type (Format) | Description |
---|---|---|---|
Code | 1 | any (default) | |
Borough | 2 | string (default) | |
Year | 3 | date (%Y-%m-%d) | |
Value | 4 | any (default) |
Use our data-cli tool designed for data wranglers:
data get https://datahub.io/london/crime
data info london/crime
tree london/crime
# Get a list of dataset's resources
curl -L -s https://datahub.io/london/crime/datapackage.json | grep path
# Get resources
curl -L https://datahub.io/london/crime/r/0.csv
curl -L https://datahub.io/london/crime/r/1.csv
curl -L https://datahub.io/london/crime/r/2.zip
If you are using R here's how to get the data you want quickly loaded:
install.packages("jsonlite", repos="https://cran.rstudio.com/")
library("jsonlite")
json_file <- 'https://datahub.io/london/crime/datapackage.json'
json_data <- fromJSON(paste(readLines(json_file), collapse=""))
# get list of all resources:
print(json_data$resources$name)
# print all tabular data(if exists any)
for(i in 1:length(json_data$resources$datahub$type)){
if(json_data$resources$datahub$type[i]=='derived/csv'){
path_to_file = json_data$resources$path[i]
data <- read.csv(url(path_to_file))
print(data)
}
}
Note: You might need to run the script with root permissions if you are running on Linux machine
Install the Frictionless Data data package library and the pandas itself:
pip install datapackage
pip install pandas
Now you can use the datapackage in the Pandas:
import datapackage
import pandas as pd
data_url = 'https://datahub.io/london/crime/datapackage.json'
# to load Data Package into storage
package = datapackage.Package(data_url)
# to load only tabular data
resources = package.resources
for resource in resources:
if resource.tabular:
data = pd.read_csv(resource.descriptor['path'])
print (data)
For Python, first install the `datapackage` library (all the datasets on DataHub are Data Packages):
pip install datapackage
To get Data Package into your Python environment, run following code:
from datapackage import Package
package = Package('https://datahub.io/london/crime/datapackage.json')
# print list of all resources:
print(package.resource_names)
# print processed tabular data (if exists any)
for resource in package.resources:
if resource.descriptor['datahub']['type'] == 'derived/csv':
print(resource.read())
If you are using JavaScript, please, follow instructions below:
Install data.js
module using npm
:
$ npm install data.js
Once the package is installed, use the following code snippet:
const {Dataset} = require('data.js')
const path = 'https://datahub.io/london/crime/datapackage.json'
// We're using self-invoking function here as we want to use async-await syntax:
;(async () => {
const dataset = await Dataset.load(path)
// get list of all resources:
for (const id in dataset.resources) {
console.log(dataset.resources[id]._descriptor.name)
}
// get all tabular data(if exists any)
for (const id in dataset.resources) {
if (dataset.resources[id]._descriptor.format === "csv") {
const file = dataset.resources[id]
// Get a raw stream
const stream = await file.stream()
// entire file as a buffer (be careful with large files!)
const buffer = await file.buffer
// print data
stream.pipe(process.stdout)
}
}
})()
This dataset was scraped from London data website.
Numbers of recorded offences, and rates of offences per thousand population, by broad crime grouping, by financial year and borough.
Rate is given as per thousand population, and are calculated using mid-year population from the first part of the financial year eg For Financial year 2008-09, mid-year estimates for 2008 are used.
Offences: These are confirmed reports of crimes being committed. All data relates to “notifiable offences” - which are designated categories of crimes that all police forces in England and Wales are required to report to the Home Office Crime rates are not available for Heathrow due to no population figures
There were changes to the police recorded crime classifications from April 2012. Therefore caution should be used when comparing sub-groups of crime figures from 2012/13 with earlier years.
Action Fraud have taken over the recording of fraud offences on behalf of individual police forces. This process began in April 2011 and was rolled out to all police forces by March 2013. Due to this change caution should be applied when comparing data over this transitional period and with earlier years.
Dataset used for this scraping have been found on Recorderd crime: Borugh Rates.
Output data is located in data
directory, it consists of two csv
files:
crime-rates.csv
recorded-offences.csv
You will need Python 3.6 or greater and dataflows library to run the script
To update the data run the process script locally:
# Install dataflows
pip install dataflows
# Run the script
python london-crime.py
Open Government Licence
You are encouraged to use and re-use the Information that is available under this licence freely and flexibly, with only a few conditions. Using Information under this licence Use of copyright and database right material expressly made available under this licence (the ‘Information’) indicates your acceptance of the terms and conditions below. The Licensor grants you a worldwide, royalty-free, perpetual, non-exclusive licence to use the Information subject to the conditions below. This licence does not affect your freedom under fair dealing or fair use or any other copyright or database right exceptions and limitations.
You may find further information here