The Sociological Map of Insecurity (SOMI), that can be accessed from MARGIN’s website, aims to display crime-related statistics geo-localised across the areas where the project fieldwork takes place. It shows results from crime victimisation surveys and police recorded crimes, disaggregated to the lowest geographical area for which we have information. Unfortunately, while the data can be displayed at the neighbourhood level for Barcelona and Budapest, we only have average rates for London, Paris, Milan and Florence. For these last cases, we also show the country-rate for the sake of comparison. In addition, we will be updating the maps with data from our fieldwork. In particular, qualitative information on the perception of insecurity, as well as the results from our own survey questionnaire, will be uploaded as soon as data is ready to be disseminated.
In this post, we aim to contrast results from the first part of the analysis, corresponding to the police recorded crimes. As it has been said, since we have different information level for the 7 cities involved in the project, we can only make a partial comparison. However, we are already working to show the same type of data for each city.
From the current SOMI, we can compare three measures: home-related crimes (expressed as a proportion of 1,000 households), robbery or theft, and crimes against personal identity, both expressed as a rate per 1,000 inhabitants. Although these measures cluster different indicators in each country, we grouped them ensuring that they express similar ideas. Indeed, the lack of harmonised data across Europe regarding crime measures is one of the main problems that this project aims to overcome.
First of all, regarding police recorded crimes, in Barcelona, where data is broken down into neighbourhoods for 2014, we observe that the highest average of home-related crimes was recorded in La Barcelona (21.8 per 1,000 households) compared to 2.1 found in Vallbona. As for robbery rates, expressed as per 1,000 inhabitants, in El Gòtic was reported the highest (31.5), and in Vallvidrera the lowest (0.5). Finally, when we observe the results of crimes against personal integrity, which include homicides –or attempted homicides-, murders or attempted, injuries, threats and sexual assault, we observe that the highest rate was recorded in the are of Zona Franca (10.5), and the lowest was found in Pedralbes (0.2). As it is shown in the map, red pointers, symbolising higher levels of insecurity, can be found along the south coast of the city, while green pointers are found in the interior parts of Barcelona (away from the sea).
In Budapest, we have information on theft, robbery and violent crimes during 2014, all expressed as a proportion of 1,000 citizens. The highest rate of theft was found in the first district (31.1), and the lowest in the 16th (11.1). For robbery and crimes against personal integrity, the highest rates were recorded in the fifth district (1.31 and 2.15 respectively), and the lowest ones can be found in the 16th (0.06 and 0.33). The social gap regarding insecurity is, therefore, clearly pictured in Budapest, with the 5th district being on the top of insecurity measures, and the 16th at the bottom. In addition, as it can be seen in the map, there is a radial distribution of crime indicators. In general, the closer to the city centre, the more insecure the area is (depicted by red flags), while the periphery areas tend to be safer (symbolised by green pointers).
In London, results for 2014 show that home-related crimes were about 16 per 1,000 households, and 0.81 -per 1,0000 citizens- crimes against personal integrity were reported in that year. Compared to average rates in all England, these figures were higher in the city, being 8.80 and 0.56 respectively at the country-level. In Paris, home-related crimes recorded an average of 8.90 in 2014, theft recorded 0.14 per 1,000 inhabitants, and violent crimes were 5.19. In contrast with the mean figures for France, people live safer in the city concerning the numbers for theft (13.7 in France), but insecurity is lower at the country level regarding the rest of indicators (in France, home-related crimes are 8.47, and crimes against personal integrity 3.81). Finally, data for Milan and Florence show that the latter is a safer place to live than the former according to police reports of 2013. In Milan, street robbery depicted 1.59 per 1,000 inhabitants, while Florence registered 0.81. In addition, home-related crimes were slightly higher in Milan than in Florence (19.95 versus 12.85), and similar differences were found in violent crimes (0.27 in Milan and 0.18 in Florence).
Second, we turn to results of crime victimisation surveys. In this case, the map shows percentages of individuals’ perception of safety at the country-level. For each country, we selected questions that were similar between them, and thus allowed us to establish some similarities regarding the results. As it can be seen in the map, in 2014 79.2% of the population in England reported feeling very safe, while only 0,4% said feeling very unsafe. In Hungary, these percentages are 60.6% and 1.6%. In contrast, people who perceived being very safe were 43.7% in Italy, and 2.3% felt very unsafe. In France, the figures were 77.1% for very safe, and 3.3% for very unsafe. Finally, we do not have data for Spain, but in the region of Catalonia, 28.5% of the population in 2013 reported feeling very safe, and 3.7% very unsafe. Although the figures are not directly comparable, due to slightly differences in the questions being asked in each survey, we can point at England and France as those countries were a higher percentage of citizens reported feeling very safe. Again, the lack of genuine comparable data is a problem that this project will close.