Analysis of street crime predictors in web open data

Zhang, Yihong; Siriaraya, Panote; Kawai, Yukiko; Jatowt, Adam

doi:10.1007/s10844-019-00587-4

Cited by 14 publications

(15 citation statements)

References 18 publications

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“…This study has demonstrated the usefulness of a strategy that combines GSV images with a machine learning technique to extract various features of the built environment, and use this information assess their relationship with crime in street segments. Prior research has highlighted the importance of measuring crime patterns at the micro-level (Zhang et al 2019;He, Páez, and Liu 2017;Weisburd, Bernasco, and Bruinsma 2009), and we have done so here.…”

Section: Discussionmentioning

confidence: 98%

“…Studies have frequently used GSV data to measure physical disorder-abandoned cars, graffiti, litter, poor conditions of lawns, abandoned houses, or broken windows (Marco et al 2017;Odgers et al 2012)-and how it might be related to crime (Curtis and Mills 2011;He, Páez, and Liu 2017;Kang and Kang 2017;Zhang et al 2019). However, these studies typically use manual evaluation methods to extract information from the GSV images in which trained researchers observe and rate each image, which also necessarily limits the geographic scope of the studies.…”

Section: Measuring These Physical Featuresmentioning

confidence: 99%

“…Another strategy that moves beyond manual evaluation strategies-and offers the possibility of measuring other features of the environment beyond disorder-is exemplified by recent studies applying machine learning technologies to analyze GSV data using deep neural networks such as AlexNet and Inception-v3 (Kang and Kang 2017;Zhang et al 2019). For instance, Kang and Kang (2017) suggested a feature-level data fusion method with environmental context based on a deep neural network (DNN) of AlexNet and showed that the DNN model is more accurate in predicting crime occurrence than other prediction models.…”

Section: Measuring These Physical Featuresmentioning

confidence: 99%

“…Similarly, Zhang et al (2019) analyzed crime predictors using open source data of GSV, Twitter, and Foursquare and concluded that open-source data could achieve significantly better crime prediction accuracy. This same study also argued that person offenses and theft are most likely to be associated with a high concentration of commercial buildings and less green areas.…”

Section: Measuring These Physical Featuresmentioning

confidence: 99%

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Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments

Hipp

Lee

et al. 2021

J Quant Criminol

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Objectives: Despite theoretical interest in how dimensions of the built environment can help explain the location of crime in micro-geographic units, measuring this is difficult.Methods: This study adopts a strategy that first scrapes images from Google Street View every 20 meters in every street segment in the city of Santa Ana, CA, and then uses machine learning to detect features of the environment. We capture eleven different features across four main dimensions, and demonstrate that their relative presence across street segments considerably increases the explanatory power of models of five different Part 1 crimes.Results: The auto-oriented measures-vehicles and pavement-were positively associated with crime rates, as was the presence of more persons in the environment. For the defensible space measures, the presence of walls has a slowing negative relationship with most crime types, whereas fences did not. And for our two greenspace measures, although terrain was positively associated with crime rates, vegetation exhibited an inverted-U relationship with two crime types.Conclusions: The results demonstrate the efficacy of this approach for measuring the built environment.

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Section: Discussionmentioning

confidence: 98%

Section: Measuring These Physical Featuresmentioning

confidence: 99%

Section: Measuring These Physical Featuresmentioning

confidence: 99%

Section: Measuring These Physical Featuresmentioning

confidence: 99%

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Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments

Hipp

Lee

et al. 2021

J Quant Criminol

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“…Given the broad coverage of these street views APIs, these tools have proved beneficial for analyzing multiple cities at once [15]. More specifically, these methods have been utilized to find the relationships between the visual appearance of streetscapes and the health of the citizens [16,17], safety and crime [18,19], and urban aesthetics [20][21][22]. Further, such studies Similarly, identifying streets from satellite and aerial images has gathered a lot of interest in recent years.…”

Section: Introductionmentioning

confidence: 99%

Identifying Streetscape Features Using VHR Imagery and Deep Learning Applications

2021

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Deep Learning (DL) based identification and detection of elements in urban spaces through Earth Observation (EO) datasets have been widely researched and discussed. Such studies have developed state-of-the-art methods to map urban features like building footprint or roads in detail. This study delves deeper into combining multiple such studies to identify fine-grained urban features which define streetscapes. Specifically, the research focuses on employing object detection and semantic segmentation models and other computer vision methods to identify ten streetscape features such as movement corridors, roadways, sidewalks, bike paths, on-street parking, vehicles, trees, vegetation, road markings, and buildings. The training data for identifying and classifying all the elements except road markings are collected from open sources and finetuned to fit the study’s context. The training dataset is manually created and employed to delineate road markings. Apart from the model-specific evaluation on the test-set of the data, the study creates its own test dataset from the study area to analyze these models’ performance. The outputs from these models are further integrated to develop a geospatial dataset, which is additionally utilized to generate 3D views and street cross-sections for the city. The trained models and data sources are discussed in the research and are made available for urban researchers to exploit.

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Themes in data mining, big data, and crime analytics

Oatley

2021

WIREs Data Min & Knowl

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This article examines the impact of new AI‐related technologies in data mining and big data on important research questions in crime analytics. Because the field is so broad, the review focuses on a selection of the most important topics. Challenges for information management, and in turn law and society, include: AI‐powered predictive policing; big data for legal and adversarial decisions; bias using big data and analytics in profiling and predicting criminality; forecasting crime risk and crime rates; and, regulating AI systems. This article is categorized under: Algorithmic Development > Spatial and Temporal Data Mining Fundamental Concepts of Data and Knowledge > Big Data Mining Technologies > Artificial Intelligence Application Areas > Data Mining Software Tools

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Analysis of street crime predictors in web open data

Cited by 14 publications

References 18 publications

Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments

Measuring the Built Environment with Google Street View and Machine Learning: Consequences for Crime on Street Segments

Identifying Streetscape Features Using VHR Imagery and Deep Learning Applications

Themes in data mining, big data, and crime analytics

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