Urban Crime Risk Prediction Using Point of Interest Data

Cichosz, Paweł

doi:10.3390/ijgi9070459

Cited by 22 publications

(8 citation statements)

References 55 publications

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“…Paweł Cichosz, et. al., [5] has discussed about the Urban Crime Risk Prediction Using Point of Interest (POI) Data. This article demonstrates how they can be combined with ML algorithms to create crime prediction models for urban areas.…”

Section: Literature Surveymentioning

confidence: 99%

Crime data anlysis using machine learning

Kiran¹,

Mounika²,

Naveen³

et al. 2022

sajet

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Crime is one of the biggest and dominating problems in our society and its prevention is an important task. Daily there are huge numbers of crimes committed frequently. This requires keeping track of all the crimes and maintaining a database for same which may be used for future reference. The current problem faced are maintaining of proper dataset of crime and analyzing this data to help in predicting and solving crimes in future. The objective of this project is to analyze dataset which consist of numerous crimes and predicting the type of crime which may happen in future depending upon various conditions. In this project, Machine Learning and data science techniques are used for crime prediction of Chicago crime data set. For this supervised classification Random Forest algorithm is used. This approach involves predicting crimes classifying, pattern detection and visualization with effective tools and technologies. Use of past crime data trends helps us to correlate factors which might help understanding the future scope of crimes. In this work, various visualizing techniques and machine learning algorithms are adopted for predicting the crime distribution over an area. In the first step, the raw datasets are processed and visualized based on the need.

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Section: Literature Surveymentioning

confidence: 99%

Crime data anlysis using machine learning

Kiran¹,

Mounika²,

Naveen³

et al. 2022

sajet

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show abstract

“…The use of OSM data to generate socio-economic indicators and urban crime risk has been studied and testified (Feldmeyer et al 2020); (Cichosz 2020). The data processing method can be used for reference and it showcases the possibility that POI data can be a good indicator of urban conditions and activities.…”

Section: Problem Statementmentioning

confidence: 99%

A POI-Based Machine Learning Method for Predicting Residents’ Health Status

Cao

Zheng

2021

Proceedings of the 2021 DigitalFUTURES

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Health environment is a key factor in public health. Since people’s health depends largely on their lifestyle, the built environment which supports a healthy living style is becoming more important. With the right urban planning decisions, it’s possible to encourage healthier living and save healthcare expenditures for the society. However, there is not yet a quantitative relationship established between urban planning decisions and the health status of the residents. With the abundance of data and computing resources, this research aims to explore this relationship with a machine learning method. The data source is from both the OpenStreetMap and American Center for Decease Control and Prevention (CDC). By modeling the Point of Interest data and the geographic distribution of health-related outcome, the research explores the key factors in urban planning that could influence the health status of the residents quantitatively. It informs how to create a built environment that supports health and opens up possibilities for other data-driven methods in this field.

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“…In [7], the authors build an ensemble learning model to predict spatial occurrences of crimes of different types. Cichosz [8] used point of interestbased data (such as bus stops, cinema halls, etc.) from geographical information systems to build a crime risk prediction model for urban areas.…”

Section: Survey Of Trends Of Supervised and Unsupervised Machine Learning Algorithms For Crime Analysismentioning

confidence: 99%

Supervised and Unsupervised Machine Learning Methodologies for Crime Pattern Analysis

Sardana¹,

Marwaha²,

Bhatnagar³

et al. 2021

IJAIA

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Crime is a grave problem that affects all countries in the world. The level of crime in a country has a big impact on its economic growth and quality of life of citizens. In this paper, we provide a survey of trends of supervised and unsupervised machine learning methods used for crime pattern analysis. We use a spatiotemporal dataset of crimes in San Francisco, CA to demonstrate some of these strategies for crime analysis. We use classification models, namely, Logistic Regression, Random Forest, Gradient Boosting and Naive Bayes to predict crime types such as Larceny, Theft, etc. and propose model optimization strategies. Further, we use a graph based unsupervised machine learning technique called core periphery structures to analyze how crime behavior evolves over time. These methods can be generalized to use for different counties and can be greatly helpful in planning police task forces for law enforcement and crime prevention.

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Urban Crime Risk Prediction Using Point of Interest Data

Cited by 22 publications

References 55 publications

Crime data anlysis using machine learning

Crime data anlysis using machine learning

A POI-Based Machine Learning Method for Predicting Residents’ Health Status

Supervised and Unsupervised Machine Learning Methodologies for Crime Pattern Analysis

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