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.

Link to data on Met Police website.

Crime stats on ONS website

Contains a record of all crime and anti social incidents from data.police.uk for the West Midlands Police at LSOA level and reported by street for Birmingham and Solihull. Data comes from a mixture of crime management systems and command and control systems.Data has been enriched by the City Observatory to include additional location data including;WardConstituency (2024)Local authorityFor full details about quality, anonymisation, collection see https://data.police.uk/about/ .

Crime severity index (violent, non-violent, youth) and weighted clearance rates (violent, non-violent), police services in British Columbia, 1998 to 2023.

These ward level well being scores present a combined measure of well-being indicators of the resident population based on 12 different indicators. Where possible each indicator score is compared with the England and Wales average, which is zero. Scores over 0 indicate a higher probability that the population on average will experience better well-being according to these measures.

Users can adjust the weight of each indicator depending on what they consider to be the more or less important, thus generating bespoke scores. This is done either by entering a number between 0 and 10. The scores throughout the spreadsheet will update automatically.

The tool combines data across a range of themes for the last five years of available data (2009-2013).

Either view the results in the online interactive tool here,

Or download the interactive spreadsheet here

The well-being scores are then presented in a ranked bar chart for each borough, and a ward map of London.

The spreadsheet also highlights wards in the top and bottom 25 per cent in London. Wards that have shown significant improvement or reduction in their scores relative to the average over the five year period are also highlighted.

Borough figures are provided to assist with comparisons.

Rankings and summary tables are included. The source data that the tool is based on is included in the spreadsheet.

The Excel file is 8.1MB.

IMPORTANT NOTE, users must enable macros when prompted upon opening the Excel spreadsheet (or reset security to medium/low) for the map to function. The rest of the tool will function without macros.

If you cannot download the Excel file directly try this zip file (2.6MB).

If you experience any difficulties with downloading this spreadsheet, please contact the London Datastore in the Intelligence Unit.

Detailed information about definitions and sources is contained within the spreadsheet.

The 12 measures included are:

Health
- Life Expectancy
- Childhood Obesity
- Incapacity Benefits claimant rate

Economic security
- Unemployment rate

Safety
- Crime rate
- Deliberate Fires

Education
- GCSE point scores

Children
- Unauthorised Pupil Absence

Families
- Children in out-of-work households

Transport
- Public Transport Accessibility Scores (PTALs)

Environment
- Access to public open space & nature

Happiness
- Composite Subjective Well-being Score (Life Satisfaction, Worthwhileness, Anxiety, and Happiness) (New data only available since 2011/12)

With some measures if the data shows a high figure that indicates better well-being, and with other measures a low figure indicates better well-being. Therefore scores for Life Expectancy, GCSE scores, PTALs, and Access to Public Open Space/Nature have been reversed so that in all measures low scores indicate probable lower well-being.

The data has been turned into scores where each indicator in each year has a standard deviation of 10. This means that each indicator will have an equal effect on the final score when the weightings are set to equal.

Why should measuring well-being be important to policy makers?
Following research by the Cabinet Office and Office for National Statistics, the government is aiming to develop policy that is more focused on ‘all those things that make life worthwhile’ (David Cameron, November 2010). They are interested in developing new and better ways to understand how policy and public services affect well-being.

Why measure well-being for local areas?
It is important for London policy makers to consider well-being at a local level (smaller than borough level) because of the often huge differences within boroughs. Local authorities rely on small area data in order to target resources, and with local authorities currently gaining more responsibilities from government, this is of increasing importance. But small area data is also of interest to academics, independent analysts and members of the public with an interest in the subject of well-being.

How can well-being be measured within small areas?
The Office for National Statistics have been developing new measures of national well-being, and as part of this, at a national and regional level, the ONS has published some subjective data to measure happiness. ONS have not measured well-being for small areas, so this tool has been designed to fill this gap. However, DCLG have published a tool that models life satisfaction data for LSOAs based on a combination of national level happiness data, and 'ACORN' data. Happiness data is not available for small areas because there are no surveys large enough for this level of detail, and so at this geography the focus is on objective indicators. Data availability for small areas is far more limited than for districts, and this means the indicators that the scores are based on are not all perfect measures of well-being, though they are the best available. However, by using a relatively high number of measures across a number of years, this increases the reliability of the well-being scores.

How can this tool be used to help policy makers?
Each neighbourhood will have its own priorities, but the data in this tool could help provide a solid evidence base for informed local policy-making, and the distribution of regeneration funds. In addition, it could assist users to identify the causes behind an improvement in well-being in certain wards, where examples of good practice could be applied elsewhere.

Differences to the previous report

This is the 2013 edition of this publication, and there is one change from 2012. Indicators of Election turnout has been replaced with a composite score of subjective well-being indicators.

Past versions are still available for 2011 and 2012. The rationale/methodology paper from 2011 is here. The scores from the 2012 spreadsheet are also available in PDF format. The scores in Intelligence Update 21-2012 are based on equal weightings across each measure.

This tool was created by the GLA Intelligence Unit. Please contact datastore@london.gov.uk for more information.

This data originates from the Public Health Outcomes tool currently presents data for available indicators for upper tier local authority levels, collated by Public Health England (PHE).

The data currently published here are the baselines for the Public Health Outcomes Framework, together with more recent data where these are available. The baseline period is 2010 or equivalent, unless these data are unavailable or not deemed to be of sufficient quality. The first data were published in this tool as an official statistics release in November 2012. Future official statistics updates will be published as part of a quarterly update cycle in August, November, February and May.

The definition, rationale, source information, and methodology for each indicator can be found within the spreadsheet.

Data included in the spreadsheet:

0.1i - Healthy life expectancy at birth
0.1ii - Life Expectancy at 65
0.1ii - Life Expectancy at birth
0.2i - Slope index of inequality in life expectancy at birth based on national deprivation deciles within England
0.2ii - Number of upper tier local authorities for which the local slope index of inequality in life expectancy (as defined in 0.2iii) has decreased
0.2iii - Slope index of inequality in life expectancy at birth within English local authorities, based on local deprivation deciles within each area
0.2iv - Gap in life expectancy at birth between each local authority and England as a whole
0.2v - Slope index of inequality in healthy life expectancy at birth based on national deprivation deciles within England
0.2vii - Slope index of inequality in life expectancy at birth within English regions, based on regional deprivation deciles within each area
1.01i - Children in poverty (all dependent children under 20)
1.01ii - Children in poverty (under 16s)
1.02i - School Readiness: The percentage of children achieving a good level of development at the end of reception
1.02i - School Readiness: The percentage of children with free school meal status achieving a good level of development at the end of reception
1.02ii - School Readiness: The percentage of Year 1 pupils achieving the expected level in the phonics screening check
1.02ii - School Readiness: The percentage of Year 1 pupils with free school meal status achieving the expected level in the phonics screening check
1.03 - Pupil absence
1.04 - First time entrants to the youth justice system
1.05 - 16-18 year olds not in education employment or training
1.06i - Adults with a learning disability who live in stable and appropriate accommodation
1.06ii - % of adults in contact with secondary mental health services who live in stable and appropriate accommodation
1.07 - People in prison who have a mental illness or a significant mental illness
1.08i - Gap in the employment rate between those with a long-term health condition and the overall employment rate
1.08ii - Gap in the employment rate between those with a learning disability and the overall employment rate
1.08iii - Gap in the employment rate for those in contact with secondary mental health services and the overall employment rate
1.09i - Sickness absence - The percentage of employees who had at least one day off in the previous week
1.09ii - Sickness absence - The percent of working days lost due to sickness absence
1.10 - Killed and seriously injured (KSI) casualties on England's roads
1.11 - Domestic Abuse
1.12i - Violent crime (including sexual violence) - hospital admissions for violence
1.12ii - Violent crime (including sexual violence) - violence offences per 1,000 population
1.12iii- Violent crime (including sexual violence) - Rate of sexual offences per 1,000 population
1.13i - Re-offending levels - percentage of offenders who re-offend
1.13ii - Re-offending levels - average number of re-offences per offender
1.14i - The rate of complaints about noise
1.14ii - The percentage of the population exposed to road, rail and air transport noise of 65dB(A) or more, during the daytime
1.14iii - The percentage of the population exposed to road, rail and air transport noise of 55 dB(A) or more during the night-time
1.15i - Statutory homelessness - homelessness acceptances
1.15ii - Statutory homelessness - households in temporary accommodation
1.16 - Utilisation of outdoor space for exercise/health reasons
1.17 - Fuel Poverty
1.18i - Social Isolation: % of adult social care users who have as much social contact as they would like
1.18ii - Social Isolation: % of adult carers who have as much social contact as they would like
1.19i - Older people's perception of community safety - safe in local area during the day
1.19ii - Older people's perception of communi

The global Under Vehicle Scanning (UVS) system market is experiencing robust growth, driven by escalating security concerns across various sectors. The market size in 2025 is estimated at $691.1 million. While the exact Compound Annual Growth Rate (CAGR) is not provided, considering the increasing adoption of UVS systems in government agencies, airports, and highway checkpoints, along with the rising demand for enhanced security measures in both public and private spaces, a conservative estimate of the CAGR for the forecast period (2025-2033) would be around 8-10%. This growth is fueled by several factors, including increasing terrorist threats, rising crime rates, and the need for improved border security. Technological advancements, such as the integration of AI and improved image processing capabilities, are further enhancing the effectiveness and efficiency of UVS systems, contributing to market expansion. The market segmentation reveals a significant demand across diverse application areas, with government agencies, airports, and highway checkpoints leading the way. The fixed UVS systems segment holds a larger market share compared to mobile systems, although the mobile segment is expected to witness faster growth owing to increased portability and flexibility. Geographically, North America and Europe are currently the major revenue contributors, due to higher security budgets and advanced infrastructure. However, the Asia-Pacific region is projected to exhibit substantial growth in the coming years, driven by rapid urbanization and increasing investments in security infrastructure across developing economies like India and China. The competitive landscape is characterized by a mix of established players and emerging companies, each striving to offer innovative solutions and cater to the evolving needs of the market. This dynamic environment fosters innovation and pushes the market towards higher levels of sophistication and effectiveness in threat detection. This in-depth report provides a comprehensive analysis of the global Under Vehicle Scanning System (UVSS) market, projected to reach a valuation of $2.5 billion by 2030. It offers invaluable insights into market dynamics, key players, emerging trends, and future growth prospects, empowering businesses and stakeholders to make informed decisions. This report leverages extensive market research and data analysis, incorporating key search terms like "under vehicle inspection system," "mobile undercarriage scanner," "airport security scanners," and "checkpoint security technology."

These ward level well being scores present a combined measure of well-being indicators of the resident population based on 12 different indicators. Where possible each indicator score is compared with the England and Wales average, which is zero. Scores over 0 indicate a higher probability that the population on average will experience better well-being according to these measures. Users can adjust the weight of each indicator depending on what they consider to be the more or less important, thus generating bespoke scores. This is done either by entering a number between 0 and 10. The scores throughout the spreadsheet will update automatically. The tool combines data across a range of themes for the last five years of available data (2009-2013). Either view the results in the online interactive tool here, Or download the interactive spreadsheet here The well-being scores are then presented in a ranked bar chart for each borough, and a ward map of London. The spreadsheet also highlights wards in the top and bottom 25 per cent in London. Wards that have shown significant improvement or reduction in their scores relative to the average over the five year period are also highlighted. Borough figures are provided to assist with comparisons. Rankings and summary tables are included. The source data that the tool is based on is included in the spreadsheet. The Excel file is 8.1MB. IMPORTANT NOTE, users must enable macros when prompted upon opening the Excel spreadsheet (or reset security to medium/low) for the map to function. The rest of the tool will function without macros. If you cannot download the Excel file directly try this zip file (2.6MB). If you experience any difficulties with downloading this spreadsheet, please contact the London Datastore in the Intelligence Unit. Detailed information about definitions and sources is contained within the spreadsheet. The 12 measures included are: Health - Life Expectancy - Childhood Obesity - Incapacity Benefits claimant rate Economic security - Unemployment rate Safety - Crime rate - Deliberate Fires Education - GCSE point scores Children - Unauthorised Pupil Absence Families - Children in out-of-work households Transport - Public Transport Accessibility Scores (PTALs) Environment - Access to public open space & nature Happiness - Composite Subjective Well-being Score (Life Satisfaction, Worthwhileness, Anxiety, and Happiness) (New data only available since 2011/12) With some measures if the data shows a high figure that indicates better well-being, and with other measures a low figure indicates better well-being. Therefore scores for Life Expectancy, GCSE scores, PTALs, and Access to Public Open Space/Nature have been reversed so that in all measures low scores indicate probable lower well-being. The data has been turned into scores where each indicator in each year has a standard deviation of 10. This means that each indicator will have an equal effect on the final score when the weightings are set to equal. Why should measuring well-being be important to policy makers? Following research by the Cabinet Office and Office for National Statistics, the government is aiming to develop policy that is more focused on ‘all those things that make life worthwhile’ (David Cameron, November 2010). They are interested in developing new and better ways to understand how policy and public services affect well-being. Why measure well-being for local areas? It is important for London policy makers to consider well-being at a local level (smaller than borough level) because of the often huge differences within boroughs. Local authorities rely on small area data in order to target resources, and with local authorities currently gaining more responsibilities from government, this is of increasing importance. But small area data is also of interest to academics, independent analysts and members of the public with an interest in the subject of well-being. How can well-being be measured within small areas? The Office for National Statistics have been developing new measures of national well-being, and as part of this, at a national and regional level, the ONS has published some subjective data to measure happiness. ONS have not measured well-being for small areas, so this tool has been designed to fill this gap. However, DCLG have published a tool that models life satisfaction data for LSOAs based on a combination of national level happiness data, and 'ACORN' data. Happiness data is not available for small areas because there are no surveys large enough for this level of detail, and so at this geography the focus is on objective indicators. Data availability for small areas is far more limited than for districts, and this means the indicators that the scores are based on are not all perfect measures of well-being, though they are the best available. However, by using a relatively high number of measures across a number of years, this increases the reliability of the well-being scores. How can this tool be used to help policy makers? Each neighbourhood will have its own priorities, but the data in this tool could help provide a solid evidence base for informed local policy-making, and the distribution of regeneration funds. In addition, it could assist users to identify the causes behind an improvement in well-being in certain wards, where examples of good practice could be applied elsewhere. Differences to the previous report This is the 2013 edition of this publication, and there is one change from 2012. Indicators of Election turnout has been replaced with a composite score of subjective well-being indicators. Past versions are still available for 2011 and 2012. The rationale/methodology paper from 2011 is here. The scores from the 2012 spreadsheet are also available in PDF format. The scores in Intelligence Update 21-2012 are based on equal weightings across each measure. This tool was created by the GLA Intelligence Unit. Please contact datastore@london.gov.uk for more information.

In 2022/23, there were ** Islamophobic hate crimes recorded in Westminster, the highest of any borough in London. The boroughs of Merton, and Havering had the fewest number of offences recorded in the same year, at *** offences each.