Exploring crime patterns in Mexico City

Piña-García, C. A.; Ramírez, Leticia Nayeli Ramírez

doi:10.1186/s40537-019-0228-x

Cited by 15 publications

(3 citation statements)

References 34 publications

(35 reference statements)

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“…Charlie Catlett et al [12] proposed a spatio-temporal crime forecasting model to detect high-risk crime regions using an auto-regressive model. Pina-Garcia et al [13] proposed that data generated from different social media platforms can be integrated to enhance big data-driven models for crime prediction. Harnessing multi-source data about public sentiments and activities for informed design is proposed by Linlin You et al [14] that addresses the process from data collection to data visualization.…”

Section: Related Workmentioning

confidence: 99%

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

Manjunatha

Annappa

2020

SCPE

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Advancement in Information Communication Technology (ICT) and the Internet of Things (IoT) has to lead tothe continuous generation of a large amount of data. Smart city projects are being implemented in various parts of the world where analysis of public data helps in providing a better quality of life. Data analytics plays a vital role in many such data-driven applications. Real-time analytics for finding valuable insights at the right time using smart city data is crucial in making appropriate decisions for city administration. It is essential to use multiple data sources as input for the analysis to achieve better and more accurate data-driven solutions. It helps in finding more accurate solutions and making appropriate decisions. Public safety is one of the major concerns in any smart city project in which real-time analytics is much useful in the early detection of valuable data patterns. It is crucial to find early predictions of crime-related incidents and generating emergency alerts for making appropriate decisions to provide security to the people and safety of the city infrastructure. This paper discusses the proposed real-time big data analytics framework with data blending approach using multiple data sources for smart city applications. Analytics using multiple data sources for a specific data-driven solution helps in finding more data patterns, which in turn increases the accuracy of analytics results. The data preprocessing phase is a challenging task in data analytics when data being ingested continuously in real-time into the analytics system. The proposed system helps in the preprocessing of real-time data with data blending of multiple data sources used in the analytics. The proposed framework is beneficial when data from multiple sources are ingested in real-time as input data and is also flexible to use any additional data source of interest. The experimental work carried out with the proposed framework using multiple data sources to find the crime-related insights in real-time helps the public safety solutions in the smart city. The experimental outcome shows that there is a significant increase in the number of identified useful data patterns as the number of data sources increases. A real-time based emergency alert system to help the public safety solution is implementedusing a machine learning-based classification algorithm with the proposed framework. The experiment is carried out with different classification algorithms, and the results show that Naive Bayes classification performs better in generating emergency alerts.

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Section: Related Workmentioning

confidence: 99%

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

Manjunatha

Annappa

2020

SCPE

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“…Other approaches have used homicide data, however, the level of granularity for these data is in the best cases by municipality or provinces/states 4,33 , or by country 31 . In both cases: lack of precision in geolocated data and large time-steps limit the description of a complex phenomenon such as the spread of crime-related homicides throughout a metropolitan area.…”

Section: Approaches To Understand Spatial and Temporal Evolution Of Vmentioning

confidence: 99%

Eight years of homicide evolution in Monterrey, Mexico: a network approach

Dorantes-Gilardi

García-Cortés

Hernández-Ramos

et al. 2020

Sci Rep

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Homicide is without doubt one of Mexico’s most important security problems, with data showing that this dismal kind of violence sky-rocketed shortly after the war on drugs was declared in 2007. Since then, violent war-like zones have appeared and disappeared throughout Mexico, causing unfathomable human, social and economic losses. One of the most emblematic of these zones is the Monterrey metropolitan area (MMA), a central scenario in the narco-war. Being an important metropolitan area in Mexico and a business hub, MMA has counted hundreds to thousands of casualties. In spite of several approaches being developed to understand and analyze crime in general, and homicide in particular, the lack of accurate spatio-temporal homicide data results in incomplete descriptions. In order to describe the manner in which violence has evolved and spread in time and space through the city, here we propose a network-based approach. For this purpose, we define a homicide network where nodes are geographical entities that are connected through spatial and temporal relationships. We analyzed the time series of homicides in different municipalities and neighborhoods of the MMA, to observe whether or not a global correlation appeared. We studied the spatial correlation between neighborhoods where homicides took place, to observe whether distance is a factor of influence in the frequency of homicides. We constructed yearly co-occurrence networks, by correlating neighborhoods with homicides happening within a same week, and counting the co-occurrences of these neighborhood pairs in 1 year. We also constructed a crime network by aggregating all data of homicides, eliminating the temporal correlation, in order to observe whether homicide clusters appeared, and what those clusters were distributed geographically. Finally, we correlated the location and frequency of homicides with roads, freeways and highways, to observe if a trend in the homicidal violence appeared. Our network approach in the homicide evolution of MMA allows us to identify that (1) analyzing the whole 86-month period, we observed a correlation between close cities, which decreases in distant places. (2) at neighborhood level, correlations are not distance-dependent, on the contrary, highest co-occurrences appeared between distant neighborhoods and a polygon formed by close neighborhoods in downtown Monterrey. Moreover, (3) An elevated number of homicides occur close to the 85th freeway, which connects MMA with the US border. (4) Some socioeconomic barriers determine the presence of homicide violence. Finally, (5) we show a relation between homicidal crime and the urban landscape by studying the distance of safe and violent neighborhoods to the closest highway and by studying the evolution of highway and crime distance over the cartel-related years and the following period. With this approach, we are able to describe the spatial and temporal evolution of homicidal crime in a metropolitan area.

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“…Stubbs-Richardson et al (2018) calculated the popularity of search phrases such as “vehicle alarm system” and “house alarm system” to represent individuals’ crime prevention searches and discovered a positive correlation between the relative volume of these queries and the rates of MVT and burglary captured in the UCR. More recently, Piña-García and Ramírez-Ramírez (2019) employed GT data and official crime data to explore the incidence criminal activity in Mexico City. Hoehn-Velasco et al (2021) examined whether COVID-19 stay-at-home orders affected crimes targeting women (sexual offenses and domestic violence).…”

Section: Introductionmentioning

confidence: 99%

Big data in crime statistics: Using Google Trends to measure victimization in designated market areas across the United States

Liu,

Wolff,

2023

Methodological Innovations

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Google Trends (GT) data could potentially supplement traditional crime measurement strategies, allowing criminologists to better understand crime statistics on a macro level. This study assesses the validity of GT crime estimates. The findings indicate that GT data are reliable for estimating MVT, larceny, and rape. Additionally, we illustrate how to use GT to identify places with high rates of unreported offenses. The results of this study demonstrate the feasibility of leveraging open-source big data such as GT to supplement traditional sources of crime data, particularly for categories of crime with substantial underreporting rates. Results suggest the GT rape measure may be a more accurate estimate of the true incidence of rape than the measure drawn from the Uniform Crime Report (UCR). The limitations associated with the use of GT to generate estimates of crime are also discussed.

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Exploring crime patterns in Mexico City

Cited by 15 publications

References 34 publications

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

Real-time Big Data Analytics Framework with Data Blending Approach for Multiple Data sources in Smart City Applications

Eight years of homicide evolution in Monterrey, Mexico: a network approach

Big data in crime statistics: Using Google Trends to measure victimization in designated market areas across the United States

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