Urenio Watch Watch: Cities

Safe Cities Index, 2015

Safe Cities IndexThe Safe Cities Index 2015 is an Economist Intelligence Unit report, sponsored by NEC. The report is based on an index composed of more than 40 quantitative and qualitative indicators. These indicators are split across four thematic categories: digital security; health security; infrastructure safety; and personal safety. Every city in the Index is scored across these four categories. The Index focuses on 50 cities based on factors such as regional representation and availability of data.

Securing public safety means addressing a wide’”and evolving’”range of risks. The Safe Cities Index aims to capture this complexity. The Index tracks the relative safety of a city across four categories: digital security, health security, infrastructure safety and personal safety. Some key findings include the following:

Safety is closely linked to wealth and economic development. Unsurprisingly,a division emerges in the Index between cities in developed markets, which tend to fall into the top half of the overall list, and cities in developing markets, which appear in the bottom half. However, wealth and ample resources are no guarantee of urban safety.

Technology is now on the frontline of urban safety, alongside people. Data are being used to tackle crime, monitor infrastructure and limit the spread of disease. As some cities pursue smarter methods of preventing’”rather than simply reacting to’”these diverse security threats, a lack of data in emerging markets could exacerbate the urban safety divide between rich and poor.

Collaboration on safety is critical in a complex urban environment. Now that a growing number of essential systems are interconnected, city experts stress the need to bring together representatives from government, business and the community before threats to safety and security strike.

In order to be able to compare data points across countries, as well as to construct aggregate scores for each country, the project team had to first make the gathered data comparable. To do so, the quantitative indicators were normalised on a scale of 0-100 using a min-max calculation, where the score is the standard deviation from the mean, with the best country scoring 100 points and the worst scoring 0.

Many of the qualitative indicators were normalised in a similar way, but direct scores from previous and ongoing EIU city indexes and rankings were used. In some instances, those scores were on a scale of 0-100. In others, a scale of 1-5 was used, with 1 being the lowest or most negative score, and 5 being the highest or most positive score.

The status indicators were normalised as a two or three-point rating. For example, “dedicated cyber security teams’ was normalised so that neither a national- or city-level cyber security team scored 0, a national team only scored 50, and a city-level team scored 100.

The index is an aggregate score of all the underlying indicators. The index is first aggregated by category’”creating a score for each category (for example, personal safety)’” and finally, overall, based on the composite of the underlying category scores. To create the underlying category scores, each underlying indicator was aggregated according to an assigned weighting. Sub-indicators are all weighted equally, as are the four main indicator categories.

Indicators of the index

1. Digital security Weight: 25%
A. Inputs
1.1.1 Privacy policy 0 ‘“ 5, 5 = strong policy EIU analysis
1.1.2 Citizen awareness of digital threats 0 ‘“ 3, 3 = very aware EIU analysis
1.1.3 Public-private partnerships 0 ‘“ 2, 2 = close partnerships EIU analysis
1.1.4 Level of technology employed 0 ‘“ 100, 100 = highest EIU Global City Competitiveness Index
1.1.5 Dedicated cyber security teams 0 = none, 1 = national only, 2 = national and city level EIU analysis
B. Outputs
1.2.1 Frequency of identity theft % EIU analysis
1.2.2. Percentage of computers infected Scale 1 ‘“ 5, 5 = most Kaspersky Lab
1.2.3 Percentage with Internet access % ITU

2. Health security Weight: 25%
A. Inputs
2.1.1 Environmental policies 0 ‘“ 100, 100 = best EIU Green Cities Index
2.1.2 Access to healthcare 0 ‘“ 100, 100 = best EIU City Liveability Index
2.1.3 No. of beds per 1,000 # Local data sources
2.1.4 No. of doctors per 1,000 # Local data sources
2.1.5 Access to safe and quality food 0 ‘“ 100, 100 = best EIU City Liveability Index
2.1.6 Quality of health services 1 ‘“ 5, 5 = best EIU City Liveability Index
B. Outputs
2.2.1 Air quality PM 2.5 levels WHO
2.2.2 Water quality 0 ‘“ 100, 100 = best EIU Green Cities Index
2.2.3 Life expectancy Years, the longer, the better Local data sources
2.2.4 Infant mortality Deaths per 1,000 births Local data sources
2.2.5 Cancer mortality rate Deaths per 100,000 Local data sources

3. Infrastructure safety Weight: 25%
A. Inputs
3.1.1 Enforcement of transport safety 0 ‘“ 10, 10 = best EIU analysis
3.1.2 Pedestrian friendliness 1 ‘“ 5, 5 = best EIU Green Cities Index
3.1.3 Quality of road infrastructure 1 ‘“ 5, 5 = best EIU City Liveability Index
3.1.4 Quality of electricity infrastructure 1 ‘“ 5, 5 = best EIU City Liveability Index
3.1.5 Disaster management/business continuity plan 1 ‘“ 5, 5 = best EIU Global City Competitiveness Index
B. Outputs
3.2.1 Deaths from natural disasters # / million / yr, average of the last five years Local data sources
3.2.2 Frequency of vehicular accidents # / million / yr Local data sources
3.2.3 Frequency of pedestrian deaths # / million / yr Local data sources
3.2.4 Percentage living in slums % UNPD

4. Personal safety Weight: 25%
A. Inputs
4.1.1 Level of police engagement 0 ‘“ 1, 1 = engagement plan, 0 = none EIU analysis
4.1.2 Community-based patrolling 0 ‘“ 1, 1 = yes, 0 = none EIU analysis
4.1.3 Available street-level crime data 0 ‘“ 1, 1 = yes, 0 = none EIU analysis
4.1.4 Use of data-driven techniques for crime 0 ‘“ 1, 1 = yes, 0 = none EIU analysis
4.1.5 Private security measures 0 ‘“ 1, 1 = yes, 0 = none EIU analysis
4.1.6 Gun regulation and enforcement 0 ‘“ 10, 10 = strict enforcement Local data sources
4.1.7 Political stability risk 0 ‘“ 100, 0 = no risk EIU Operational Risk Model
B. Outputs
4.2.1 Prevalence of petty crime 1 ‘“ 5, 5 = high prevalence EIU City Liveability Index
4.2.2 Prevalence of violent crime 1 ‘“ 5, 5 = high prevalence EIU City Liveability Index
4.2.3 Criminal gang activity US$ billion Havoscope Global Black Market Data
4.2.4 Level of corruption 0 ‘“ 100, 100 = least corrupt EIU City Competitiveness Index
4.2.5 Rate of drug use % of population estimated to be users UN Office on Drugs and Crime
4.2.6 Frequency of terrorist attacks Average annual attacks over last 10 years Global Terrorism Database
4.2.7 Gender safety Incidences of rape in latest year Local data sources
4.2.8 Perceptions of safety 0 ‘“ 100, 100 = perceived as most safe

Source: The Economist Safe Cities Index

Download: The White Paper