CrimeTelescope: crime hotspot prediction based on urban and social media data fusion

Crime is a complex social issue impacting a considerable number of individuals within a society. Preventing and reducing crime is a top priority in many countries. Given limited policing and crime reduction resources, it is often crucial to identify effective strategies to deploy the available resources. Towards this goal, crime hotspot prediction has previously been suggested. Crime hotspot prediction leverages past data in order to identify geographical areas susceptible of hosting crimes in the future. However, most of the existing techniques in crime hotspot prediction solely use historical crime records to identify crime hotspots, while ignoring the predictive power of other data such as urban or social media data. In this paper, we propose CrimeTelescope, a platform that predicts and visualizes crime hotspots based on a fusion of different data types. Our platform continuously collects crime data as well as urban and social media data on the Web. It then extracts key features from the collected data based on both statistical and linguistic analysis. Finally, it identifies crime hotspots by leveraging the extracted features, and offers visualizations of the hotspots on an interactive map. Based on real-world data collected from New York City, we show that combining different types of data can effectively improve the crime hotspot prediction accuracy (by up to 5.2%), compared to classical approaches based on historical crime records only. In addition, we demonstrate the usability of our platform through a System Usability Scale (SUS) survey on a full prototype of CrimeTelescope

Crime prediction and monitoring in Porto, Portugal, using machine learning, spatial and text analytics

Crimes are a common societal concern impacting quality of life and economic growth. Despite the global decrease in crime statistics, specific types of crime and feelings of insecurity, have often increased, leading safety and security agencies with the need to apply novel approaches and advanced systems to better predict and prevent occurrences. The use of geospatial technologies, combined with data mining and machine learning techniques allows for significant advances in the criminology of place. In this study, official police data from Porto, in Portugal, between 2016 and 2018, was georeferenced and treated using spatial analysis methods, which allowed the identification of spatial patterns and relevant hotspots. Then, machine learning processes were applied for space-time pattern mining. Using lasso regression analysis, significance for crime variables were found, with random forest and decision tree supporting the important variable selection. Lastly, tweets related to insecurity were collected and topic modeling and sentiment analysis was performed. Together, these methods assist interpretation of patterns, prediction and ultimately, performance of both police and planning professionals

Novel evaluation metrics for sparse spatio-temporal point process hotspot predictions - a crime case study

Many physical and sociological processes are represented as discrete events in time and space. These spatio-temporal point processes are often sparse, meaning that they cannot be aggregated and treated with conventional regression models. Models based on the point process framework may be employed instead for prediction purposes. Evaluating the predictive performance of these models poses a unique challenge, as the same sparseness prevents the use of popular measures such as the root mean squared error. Statistical likelihood is a valid alternative, but this does not measure absolute performance and is therefore difficult for practitioners and researchers to interpret. Motivated by this limitation, we develop a practical toolkit of evaluation metrics for spatio-temporal point process predictions. The metrics are based around the concept of hotspots, which represent areas of high point density. In addition to measuring predictive accuracy, our evaluation toolkit considers broader aspects of predictive performance, including a characterisation of the spatial and temporal distributions of predicted hotspots and a comparison of the complementarity of different prediction methods. We demonstrate the application of our evaluation metrics using a case study of crime prediction, comparing four varied prediction methods using crime data from two different locations and multiple crime types. The results highlight a previously unseen interplay between predictive accuracy and spatio-temporal dispersion of predicted hotspots. The new evaluation framework may be applied to compare multiple prediction methods in a variety of scenarios, yielding valuable new insight into the predictive performance of point process-based prediction

Fire Regime in a Peatland Restoration Area: Lesson from Central Kalimantan

Peat fires have caused carbon emissions and damage to local and regional communities in Indonesia. An effective fire prevention system is required for mitigating climate change and enabling sustainable development of peatlands. This study examined the fire regime in a peatland restoration area in Central Kalimantan in order to assist the establishment of a fire prevention system. The fire regime was analysed using spatial-temporal analysis, land cover change mapping, and logistic regression analysis. Spatial-temporal analysis was done using monthly Niño 3.4 sea surface temperature anomalies, daily rainfall, and MODIS Active Fire (MCD14DL) hotspots from 2006 to 2015. Land cover change was mapped using Landsat imagery from2014, 2015 and 2016. Logistic regression analysis was conducted to identify significant factors that increase fire risk. The temporal analysis showed that the strongest El Niño occurred in 2015, when the region experienced a 140-days drought period. The highest number of hotspots was also observed in this year, with hotspots concentrated in the latter half of drought period. Moreover, spatial analysis using Kernel Density Estimation (KDE) showed fire recur in degraded areas. The logistic regression analysis used topographic and proximity factors, land cover classes, and soil types as independent variables. It showed that fire in 2014 and 2015 was associated with several land cover classes and was related to historical fire occurrence areas based on KDE results. Several area of peatland forests burned in 2015 and occurred at the forest edge areas located near cultivated or degraded land (e.g. shrubland) and oil palm plantations. Based on the results, the fire regime in the study area is characterized by fires that occurring/recurring in relation to climatic conditions, especially drought periods, and are typically located in cultivated or degraded land cover classes. These parameters should be considered in developing a fire prevention system in the restoration area.Rezim Kebakaran Hutan dan Lahan di Area Restorasi Lahan Gambut: Studi dari Kalimantan TengahIntisariKebakaran di lahan gambut menyebabkan emisi karbon dan kerusakan sistem kehidupan masyarakat lokal dan regional. Sistem pencegahan kebakaran yang efektif diperlukan untuk mitigasi perubahan iklim serta mendorong pembangunan lahan dan hutan yang lestari di kawasan gambut. Studi ini meneliti tentang rezim kebakaran hutan dan lahan di suatu kawasan restorasi gambut di Kalimantan Tengah. Rezim kebakaran hutan dan lahan dianalisis menggunakan analisis spasial-temporal, perubahan tutupan lahan, dan regresi logistik. Analisis spasial-temporal menggunakan parameter nilai rata-rata sea surface temperature (SST) bulanan, curah hujan harian, dan hotspot dari MODIS Active Fire (MCD14DL) tahun 2006-2016. Perubahan tutupan lahan dipetakan dengan analisis citra Landsat tahun 2014, 2015 dan 2016. Regresi logistik digunakan untuk menganalisis faktor yang berpengaruh pada peningkatan resiko kebakaran. Analisis temporal terhadap nilai SST tahun 2006-2016 menunjukkan bahwa El- Niño terparah terjadi di tahun 2015 yang memiliki hari tanpa hujan selama 140 hari berturut-turut dan ditemukan titik hotspot terbanyak. Kernel Density Estimation (KDE) digunakan dalam analisis spasial dan hasilnya menunjukkan bahwa kebakaran terjadi dan dapat berulang di area terdegradasi. Regresi logistik  menggunakan parameter yang terdiri faktor topografis, kedekatan dengan sungai/kanal, tipe penutupan lahan, serta jenis tanah. Hasil analisis menunjukkan bahwa kebarakan tahun 2014 dan 2015 berhubungan dengan beberapa tipe tutupan lahan di area yang secara historis pernah terbakar berdasarkan analisis KDE, sehingga area tersebut terindikasi telah terdegradasi sebelumnya. Beberapa area hutan di lahan gambut juga mengalami kebakaran pada tahun 2015 khususnya di area tepi hutannya. Berdasarkan hasil, rezim kebakaran di area studi dapat dijelaskan bahwa kebakaran terjadi dan dapat berulang karena pengaruh iklim

Midblock Crash Analysis Using Short Segments and Precisely Geocoded Crashes

This research focused on evaluating how crash geocoding has improved over the years and how this enhanced spatial accuracy of crashes can potentially lead to a new paradigm for midblock crash safety analysis. Robust midblock safety analysis exhibits special challenges because methods of locating crashes have historically not been very accurate. One objective of this research was to assess how the accuracy of crashes has improved over time and what the current state of the art is. The second objective focused on using segment lengths less than the Highway Safety Manual (HSM) recommended minimum of 0.1 miles for statewide screening of midblock crash locations to identify site specific locations with high crash incidence through a peak search methodology. The research clearly indicates that the use of segments of 0.1 miles (or greater) in many instances’ “hides” the severity of a single location if the rest of the segment has few or no additional crashes. The research also evaluated a sliding window approach using short segments. Based on the analysis, the short segment peak search method is recommended for use by state agencies as a network screening approach because it is much less complex to implement than the sliding window approach, locations can be easily ranked, and direct comparisons can be made of segment crash incidence over multiple years. The final objective of this research was to compare the short segment peak search approach to other HSM methods. The results of the comparison revealed similar results at the highest priority level and thus the former can be used as an alternative in case of insufficient data on driveway and roadway characteristics. This research shows that improvements in crash geocoding makes short-segment peak search network screening viable for segment lengths less than 0.1 miles. By using short segment network screening, segments of high crash incidence can be displayed with overlayed crashes at their actual crash locations which can minimize the need for developing collision diagrams. Secondly, one of the hypotheses is that the current intersection to intersection process aggregates crashes to long segments which can mask the crash severity of point locations

Examining the extent to which hotspot analysis can support spatial predictions of crime

The premise that where crime has occurred previously, informs where crime is likely to occur in the future has long been used for geographically targeting police and public safety services. Hotspot analysis is the most applied technique that is based on this premise – using crime data to identify areas of crime concentration, and in turn predict where crime is likely to occur. However, the extent to which hotspot analysis can accurately predict spatial patterns of crime has not been comprehensively examined. The current research involves an examination of hotspot analysis techniques, measuring the extent to which these techniques accurately predict spatial patterns of crime. The research includes comparing the prediction performance of hotspot analysis techniques that are commonly used in policing and public safety, such as kernel density estimation, to spatial significance mapping techniques such as the Gi* statistic. The research also considers how different retrospective periods of crime data influence the accuracy of the predictions made by spatial analysis techniques, for different periods of the future. In addition to considering the sole use of recorded crime data for informing spatial predictions of crime, the research examines the use of geographically weighted regression for determining variables that statistically correlate with crime, and how these variables can be used to inform spatial crime prediction. The findings from the research result in introducing the crime prediction framework for aiding spatial crime prediction. The crime prediction framework illustrates the importance of aligning predictions for different periods of the future to different police and prevention response activities, with each future time period informed by different spatial analysis techniques and different retrospective crime data, underpinned with different theoretical explanations for predicting where crime is likely to occur

Predictive policing : a comparative study of three hotspot mapping techniques

Indiana University-Purdue University Indianapolis (IUPUI)Law enforcement agencies across the U.S. use maps of crime to inform their practice and make efforts to reduce crime. Hotspot maps using historic crime data can show practitioners concentrated areas of criminal offenses and the types of offenses that have occurred; however, not all of these hotspot crime mapping techniques produce the same results. This study compares three hotspot crime mapping techniques and four crime types using the Predictive Accuracy Index (PAI) to measure the predictive accuracy of these mapping techniques in Marion County, Indiana. Results show that the grid hotspot mapping technique and crimes of robbery are most predictive. Understanding the most effective crime mapping technique will allow law enforcement to better predict and therefore prevent crimes

Visual Analytics Methods for Exploring Geographically Networked Phenomena

abstract: The connections between different entities define different kinds of networks, and many such networked phenomena are influenced by their underlying geographical relationships. By integrating network and geospatial analysis, the goal is to extract information about interaction topologies and the relationships to related geographical constructs. In the recent decades, much work has been done analyzing the dynamics of spatial networks; however, many challenges still remain in this field. First, the development of social media and transportation technologies has greatly reshaped the typologies of communications between different geographical regions. Second, the distance metrics used in spatial analysis should also be enriched with the underlying network information to develop accurate models. Visual analytics provides methods for data exploration, pattern recognition, and knowledge discovery. However, despite the long history of geovisualizations and network visual analytics, little work has been done to develop visual analytics tools that focus specifically on geographically networked phenomena. This thesis develops a variety of visualization methods to present data values and geospatial network relationships, which enables users to interactively explore the data. Users can investigate the connections in both virtual networks and geospatial networks and the underlying geographical context can be used to improve knowledge discovery. The focus of this thesis is on social media analysis and geographical hotspots optimization. A framework is proposed for social network analysis to unveil the links between social media interactions and their underlying networked geospatial phenomena. This will be combined with a novel hotspot approach to improve hotspot identification and boundary detection with the networks extracted from urban infrastructure. Several real world problems have been analyzed using the proposed visual analytics frameworks. The primary studies and experiments show that visual analytics methods can help analysts explore such data from multiple perspectives and help the knowledge discovery process.Dissertation/ThesisDoctoral Dissertation Computer Science 201