MASTER IN CITY & TECHNOLOGY Internet of People
2018/19 CRIME BUSTERS
BARCELONA
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MASTER IN CITY & TECHNOLOGY Thesis Project Title: CRIME BUSTERS
Research Studio: Internet of People
Faculty: Luis Falcรณn Faculty Assistant: Diego Pajarito
Sarine Bekarian Luna Nagatomo Polina Skorina
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CONTENTS
00 - Abstract
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01 - Introduction
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Victimization index Research objective Dataset
02 - Analysis
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Unemployment Income Density of population Immigrants Hotels overnights Airbnb overnights Police patrol
03 - Conclusion
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04 - Policies
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05 - References
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00 ABSTRACT Since the Olympic games in 1992, Barcelona has been experiencing an exponential growth in tourism. With the rise of global middle class, which is projected to increase from today’s 30% to 60% of the global population in 2030, this trend is expected to continue. Barcelona has also witnessed growth in long-term visitors and immigrants alike, attracting talents from around the world, a phenomena of an attractive global city enabled by the digital revolution. This phenomena has created new challenges in physical coexistence among its inhabitants: permanent, temporary and short term. Increase in crime is many of the growing challenges the city is facing in order to adjust to the rapidly changing landscape of the city. This project used open data and scrapped data to investigate the factors affecting crime in Barcelona and to propose new urban policies for the digital society.
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VICTIMIZATION INDEX Source : Barcelona City Council’s Victimisation Survey 2018
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01 INTRODUCTION Barcelona Crime in Barcelona has been increasing in the past two decades as shown in the victimization index. This coincides with the 1992 Olympic games and the rapid increase in tourists and visitors that followed. It is important to note that Barcelona was hugely successful in decreasing crime in the 1980’s thanks to the urban regeneration plan which took place as a part of the democratic transition after the death of Franco. In 2017, the victimization index reached the same rate as the worst year of the 1980s. It is high time to re-examine urban policies to reduce crime in Barcelona taking into account of the phenomena associated with global cities and digital society.
“Reports of theft jump 22% in Barcelona but fall in other major Spanish cities”
- El Pais 23 of October 2018
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CRIME FACTORS Source : UNODC - Handbook on the Crime Prevention Guide
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Introduction
Research objective According to the United Nations Office on Drug and Crime (UNODC), multiple factors influence crime including situations and environments related to individual, family, community, national society and global society. Some are related to values and norms, while others are related to policies, economy and politics. This project focused on the national and global society, in particular, unemployment rate, income inequality, population density, immigration, and tourism in order to investigate the factors affecting crime in Barcelona.
WHAT ARE THE FACTORS AFFECTING CRIME IN BARCELONA?
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DATASETS Source : Open Data Barcelona
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Introduction
Datasets
The project used monthly data of 73 neighborhoods over three years (2016-2018) from GuĂ rdia Urbana de Barcelona (managed incidents), Instituto Nacional de Estadistica (overnight hotel tourists), and Airbnb (overnight Airbnb tourists), among others, to analyze the correlation between criminal incidents and factors affecting crime. The managed incidents dataset from GuĂ rdia Urbana consisted of 97 different reported crimes and accidents. Relevant incidents were selected and categorized into 4 categories: personal crime, property crime, drug, and public disturbances and drug.
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02 ANALYSIS The analysis looked into the correlation of the four different categories of criminal incidents (personal, property, drug and public disturbance) and factors that are potentially affecting crime. The correlation is visualized in maps, scatter plots and line graphs. Map: The radius of the points on the maps represent the number of incidents reported in each neighbourhood in a given time period. The gradient of the neighbourhoods in the maps represent the factors and their intensity that are potentially affecting crime. Scatter plot: The scatter plots graphically represent the correlation between crime incidents (Y axis) and the factors that are potentially affecting crime (X axis). If the observations show an uphill pattern as they move from left to right, it indicates that there is a positive correlation.
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On the other hand, if the observations show a downhill pattern, this indicates that there is a negative correlation. If the observations do not resemble any kind of pattern, there is no correlation. R2: R squared is the linear regression result of the two data sets: crime incidents and factors that are potentially affecting crime. The percentage represents the degree on which the factors of crime can explain the variance in crime incidents. For example, if the R squared is 80%, it means that the selected factor can explain 80% of the variance in crime incidents, showing a high correlation between the two datasets. Line graph: The line graphs show the correlation of crime incidents and factors affecting crime over time. They visualize the seasonal variation of crime incidents and factors affecting crime over one year.
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UNEMPLOYMENT X INCIDENTS
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UNEMPLOYMENT X INCIDENTS
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Analysis
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UNEMPLOYMENT X INCIDENTS
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Analysis
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INCOME X INCIDENTS
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INCOME X INCIDENTS
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Analysis
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INCOME X INCIDENTS
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Analysis
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DENSITY OF POPULATION X INCIDENTS
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DENSITY OF POPULATION X INCIDENTS
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Analysis
population per ha
population per ha
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DENSITY OF POPULATION X INCIDENTS
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number of public disturbance incidents
number of drug incidents
Analysis
population per ha
population per ha
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IMMIGRANTS X INCIDENTS
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IMMIGRANTS X INCIDENTS
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Analysis
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IMMIGRANTS X INCIDENTS
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Analysis
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HOTEL OVERNIGHTS X INCIDENTS
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HOTEL OVERNIGHTS X INCIDENTS
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Analysis
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HOTEL OVERNIGHTS X INCIDENTS
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Analysis
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HOTEL OVERNIGHTS X INCIDENTS
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Analysis
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AIRBNB OVERNIGHTS X INCIDENTS
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AIRBNB OVERNIGHTS X INCIDENTS
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Analysis
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AIRBNB OVERNIGHTS X INCIDENTS
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Analysis
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AIRBNB OVERNIGHTS X INCIDENTS
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Analysis
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POLICE PATROL X INCIDENTS
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POLICE PATROL X INCIDENTS
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Analysis
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POLICE PATROL X INCIDENTS
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Analysis
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CONCLUSION OF REGRESSIONS
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NONE
LOW
MEDIUM
HIGH
0-20
20-40
40-60
60-80
Analysis
Regression result
Unemployment rate, income and population density did not have correlation with crime incidents. Immigration had medium R squared value, but had a high P-value suggesting that immigration is not a reliable indicator that explains the variance in crime incidents. On the other hand, hotel and Airbnb overnights had medium to high correlation with personal crime, property crime and public disturbance. Hotel overnights showed high correlation with property crime while Airbnb overnights had high correlation with public disturbance. Police patrol hours had medium to high correlation with crime incidents suggesting that police patrol hours are more or less meeting the needs of the city. The observed outliers were all from Cituat Vella district suggesting that more police patrol is needed in the particular district.
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CHOSEN NEIGHBOURHOODS
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Analysis
Hotel VS Airbnb
Further analysis was carried out to examine the difference between hotel and Airbnb and their different impact on crime. The map visualizes the locations of tourist attractions, hotels and Airbnb apartments. Hotels are concentrated around tourist attractions, event venues and business areas. Airbnb apartments are located in a similar location to that of hotels but more distributed across the city. In addition, 5 different neighbourhoods were selected according to the existence or non-existence of hotel and Airbnb apartments. These neighbourhoods were compared taking the density of different inhabitants per 1 hectare of public space and their crime incidents.
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# 1 - EL RAVAL INCIDENTS 1 352
TOTAL AREA - 109,8 HA UNBUILT - 39,4 HA
locals - 41549 immigrants - 5399 hotels - 6938 airbnb - 2636
locals - 1055 immigrants - 137 hotels - 176 airbnb - 67
1 HA
# 3 - LA BARCELONETA INCIDENTS 436 locals -12985 immigrants - 1754 hotels - 1863 airbnb - 670
TOTAL AREA - 131,4 HA UNBUILT - 65,2 HA
locals - 199 immigrants - 27 hotels - 29 airbnb - 10
1 HA
# 6 - LA SAGRADA FAMILIA INCIDENTS 447 locals -47742 immigrants - 3593 hotels - 483 airbnb - 2298
locals -997 immigrants - 75 hotels - 10 airbnb - 48
1 HA
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TOTAL AREA - 105,1 HA UNBUILT - 47,9 HA
# 6 - LES CORTS
TOTAL AREA - 141,3 HA UNBUILT - 68,5 HA
INCIDENTS 202
locals -637 immigrants - 34 hotels - 34 airbnb -5
locals -43642 immigrants - 2297 hotels - 2326 airbnb - 334
1 HA
# 51- VERDUN
TOTAL AREA - 23,7 HA UNBUILT - 8,7 HA
INCIDENTS 79 locals -11740 immigrants - 670 hotels - 0 airbnb - 26
locals -1249 immigrants - 77 hotels - 0 airbnb -3
1 HA
locals immigrants hotels airbnb 1 icon = 20 people
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03 CONCLUSION Five neighbourhoods namely, El Raval, Les Courts, Sagrada Familia, Verdun and La Barceloneta were compared to further examine the different impact of hotels and Airbnb on crime incidents in the city and to further confirm the regression analysis earlier conducted. El Raval vs Verdun: The density of local population does not affect the number of crime incidents. Verdun has high local population density, but crime incidents is low. La Sagrada Familia vs Verdun: The density of immigrant population does not affect the number of crime incidents. The two neighbourhood has the same density of immigrants but the crime incidents are the same. Verdun vs other neighbourhoods: The density of tourists, regardless of hotel or Airbnb, has high correlation with crime incidents.
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Les Courts vs Sagrada Familia: Hotels and Airbnb apartments are located in similar locations, as such, it is difficult to derive a clear conclusion on their different impact on crime incidents. In general, they both have medium to high correlation with crime incidents. In the earlier regression analysis, hotel overnights had higher correlation with property crime while Airbnb overnights had higher correlation with public disturbance. Les Courts vs La Barceloneta: Crime incidents are higher in La Barceloneta although the two neighbourhoods have the same tourist density. This difference may be explained by the existence of more tourist attractions in La Barceloneta, which increases the tourists density in public space during the day. If real time data on crowd level in public space and geolocated crime incidents were readily available, a more accurate analysis on crime would have been possible. Based on the above conclusion, urban policies will be proposed in the next chapter.
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04 POLICIES Structure •
Streamline police structure among Guàrdia Urbana de Barcelona (GUB), Mossos d’Esquadra, and Guardia Civil.
Law •
Review the law in place, especially for petty crimes.
Human Resource •
Re-allocate resources, include police patrol, according to real time crime prediction.
Technology •
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Improve the rate of crime being reported for more accurate security, safety and coexistence planning and management. Collect real time data on crime and crowd in public space to predict and prevent crime. Use technology to make data available for police, city council, residents and visitors.
Tourism Management •
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Create a win-win situation with vacation rental platforms through data sharing, joint research and policy formulation. Review PEUAT (Special Urban Plan for Tourist Accommodation) and HUT license management to re-distribute tourist accommodation. Decrease the pressure of tourists by promoting experience based tourism in non-residential areas and outside of the city of Barcelona. Increase pedestrian priority streets and public space in crowded areas using roads and parking spaces which will become obsolete in the near future.
Citizen engagement • •
Engage residents and visitors in the data collection of crime. Increase awareness of high crime area and time, especially for visitors who are not familiar with the locality.
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PROCESS OF POLICY
EXAMPLE
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Policy
Policy implementation
Technology and Citizen Engagement The proposed policies on technology and citizen engagement could be implemented in the following manner. First, real time data on population density in public space will be gathered using the most appropriate technology. Second, the data collected will be used to inform people, especially tourists, on the crowd level of public space in the city. Thirdly, people will be able to report crime on their digital devices and alert others of the crime they have experienced. Lastly, real-time data on crowd in public space and geolocated crime will be aggregated as big data for the city council to use for better urban management, for police to effectively prevent crime, and for tourists and residents to navigate the city safer.
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OPTION # 1
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step 1- pin the location
step 2 - label incident
step 3- describe incident
step 4- publish incident
Policy
OPTION # 2
step 1- choose location
step 2- look at the crowd level
attempt to pickpocketing 10 minutes ago
pickpocketing 5 minutes ago
step 3- look at the alert
step 4- choose another route 77
05 REFERENCES Articles • Barcelona Tourism for 2020 / • https://ajuntament.barcelona. cat/turisme/sites/default/files/ barcelona_tourism_for_2020. • ESTADISTICA / http://www.bcn.cat/ estadistica/angles/dades/anuari/cap08/ index.htm
• Handbook on the • crime prevention guidelines /
https://www.unodc.org/pdf/criminal_ justice/Handbook_on_Crime_Prevention_ Guidelines_-_Making_them_work.pdf
• INSIDE AIRBNB / http://insideairbnb.com/ • INFO BARCELONA / https://www.barcelona.
cat/infobarcelona/en/erc-gets-the-most-votes-inthe-municipal-elections-for-2019_816779.html
• INE / http://www.ine.es/ • Managing Tourism • in Barcelona / • https://haroldgoodwin.info/ • PEUAT / http://ajuntament.barcelona.cat/plaallotjaments-turistics/es/
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Political Parties • CIUDADANOS / https://www.ciudadanos-cs. org/
• ESQUERRA REPUBLICANA / https://www. esquerra.cat/ca/programes-electorals
• EN COMU / https://barcelonaencomu.cat/es/ programa/navega/tema/324
• JUNTS PER CETALUNYA / https://
juntspercatalunya.cat/barcelona/programaelectoral/
• PP / https://www.pp.es/conocenos/programas • PSC / http://www.socialistes.cat/
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