Exploratory Data Analysis And Crime Prediction In San Francisco

Crime has been prevalent in our society for a very long time and it continues to be so even today. The San Francisco Police Department has continued to register numerous such crime cases daily and has released this data to the public as a part of the open data initiative. In this paper, Big Data analysis is used on this dataset and a tool that predicts crime in San Francisco is provided. The focus of the project is to perform an in-depth analysis of the major types of crimes that occurred in the city, observe the trend over the years, and determine how various attributes, such as seasons, contribute to specific crimes. Furthermore, the proposed model is described that builds on the results of the performed predictive analytics, by identifying the attributes that directly affect the prediction. More specifically, the model predicts the type of crime that will occur in each district of the city. After preprocessing the dataset, the problem reduced to a multi-class classification problem. Various classification techniques such as K-Nearest Neighbor, Multi-class Logistic Regression, Decision Tree, Random Forest and Naïve Bayes are used. Lastly, our results are experimentally evaluated and compared against previous work.The proposed model finds applications in resource allocation of law enforcement in a Smart City

CRIME RATE PREDICTION USING THE RANDOM FOREST ALGORITHM

  An act that creates crimes punishable by law is characterized as a crime. Rape, fraud, terrorism, kidnapping, burglary, murder, and other crimes are common in Nigeria. Examples are cybercrime, bribery and corruption, robbery, money laundering, among other crimes. Crime is a harmful and widespread social issue that affects individuals all around the world. The rate of crime has risen dramatically in recent years. To cut down on crime, at any rate, law enforcements must take preventative actions. To protect society against crime, modern systems and new technologies are required. Although accurate real-time crime study is on aid in reducing crime rates, they are nonetheless useless. As crime occurrences are dependent on, this is a difficult subject for the scientific community to solve. Therefore, this paper proposes machine learning algorithm to indicate the frequency and pattern of crimes based on the data collected and to show the extent of crime in a particular region. Various visualization approaches and machine learning algorithms are used in this study to anticipate the crime distribution over a large area. In the first stage, raw datasets were processed and visualized according to the requirements. Then, to extract knowledge from these massive datasets, machine learning methods were deployed and uncover hidden patterns in the data, which were then utilized to investigate and report on crime patterns, It is beneficial to crime analysts. Investigate these crime networks using a variety of interactive crime visualizations. As a result, it is helpful in crime prevention

Spatio-temporal variations in the urban rhythm: the travelling waves of crime

This is the final version. Available from EDP Sciences via the DOI in this record.In the last decades, the notion that cities are in a state of equilibrium with a centralised organisation has given place to the viewpoint of cities in disequilibrium and organised from bottom to up. In this perspective, cities are evolving systems that exhibit emergent phenomena built from local decisions. While urban evolution promotes the emergence of positive social phenomena such as the formation of innovation hubs and the increase in cultural diversity, it also yields negative phenomena such as increases in criminal activity. Yet, we are still far from understanding the driving mechanisms of these phenomena. In particular, approaches to analyse urban phenomena are limited in scope by neglecting both temporal non-stationarity and spatial heterogeneity. In the case of criminal activity, we know for more than one century that crime peaks during specific times of the year, but the literature still fails to characterise the mobility of crime. Here we develop an approach to describe the spatial, temporal, and periodic variations in urban quantities. With crime data from 12 cities, we characterise how the periodicity of crime varies spatially across the city over time. We confirm one-year criminal cycles and show that this periodicity occurs unevenly across the city. These ‘waves of crime’ keep travelling across the city: while cities have a stable number of regions with a circannual period, the regions exhibit non-stationary series. Our findings support the concept of cities in a constant change, influencing urban phenomena—in agreement with the notion of cities not in equilibrium.Leibniz AssociationArmy Research OfficeScience Without Borders program (CAPES, Brazil

Multivariate Spatiotemporal Hawkes Processes and Network Reconstruction

There is often latent network structure in spatial and temporal data and the tools of network analysis can yield fascinating insights into such data. In this paper, we develop a nonparametric method for network reconstruction from spatiotemporal data sets using multivariate Hawkes processes. In contrast to prior work on network reconstruction with point-process models, which has often focused on exclusively temporal information, our approach uses both temporal and spatial information and does not assume a specific parametric form of network dynamics. This leads to an effective way of recovering an underlying network. We illustrate our approach using both synthetic networks and networks constructed from real-world data sets (a location-based social media network, a narrative of crime events, and violent gang crimes). Our results demonstrate that, in comparison to using only temporal data, our spatiotemporal approach yields improved network reconstruction, providing a basis for meaningful subsequent analysis --- such as community structure and motif analysis --- of the reconstructed networks

A Proposed Bi-layer Crime Prevention Framework Using Big Data Analytics

The future of science and technology sounds very promising. The need to adopt new technologies while navigating towards industry 4.0 has changed the perceptions of law enforcement agency to contend against criminal minds. It is sad but true that the conventional crime prevention system followed by government agencies is not effective for long-term implications. With advanced technologies that constantly generate and exchange data, big data analytics can be applied to predict and prevent crime from happening. However, dealing with the overwhelming amount of complex and heterogeneous crime-related data is never an easy task. Additionally, there are many data analytical techniques and each of them has its own strengths and weaknesses. In order to identify the most efficient techniques, recent literature is reviewed to spotlight the trend as well as to shed light on the research gaps and challenges in various areas. The areas include crime data collection and preprocessing, crime data analysis, crime prediction and crime prevention. These techniques are further analyzed by considering the advantages and disadvantages which then provides insight to propose a bi-layer crime prevention framework. The first layer intends to support the law enforcement agency’s daily operation while the second layer serves as a countermeasure for first layer. Both layers aim to reduce the crime rate by involving law enforcement agency through the utilization of various big data sources and techniques effectively. The proposed crime prevention framework will progressively collect data to deter criminal behavior for city’s environmental design. Ultimately, a safe and secure city is molded in the near future

Radiative Transfer in a Clumpy Universe: III. The Nature of Cosmological Ionizing Sources

The history of the transition from a neutral intergalactic medium (IGM) to one that is almost fully ionized can reveal the character of cosmological ionizing sources. We study the evolution of the volume filling factor of HII and HeIII regions in a clumpy IGM, and discuss the implications for rival reionization scenarios of the rapid decline in the space density of radio-loud quasars and of the large population of star-forming galaxies recently observed at z>3. The hydrogen component in a highly inhomogeneous universe is completely reionized when the number of photons emitted above 1 ryd in one recombination time equals the mean number of hydrogen atoms. If stellar sources are responsible for keeping the IGM ionized at z=5, the rate of star formation at this epoch must be comparable or greater than the one inferred from optical observations of galaxies at z=3, and the mean metallicity per baryon in the universe of order 1/500 solar. An early generation of stars in dark matter halos with circular velocities v_circ=50 km/s, possibly one of the main source of UV photons at high-z, could be detectable with the Next Generation Space Telescope. Models in which the quasar emissivity declines rapidly at z>3 predict a late HeII reionization epoch, a feature that could explain the recent detection of patchy HeII Lyman-alpha at z=2.9 by Reimers et al. (1997) and the abrupt change observed by Songaila (1998) at about the same epoch of the SiIV/CIV ratio, but appear unable to provide the required number of hydrogen-ionizing photons at z=5.Comment: LaTeX, 29 pages, 5 figures, submitted to the Ap

THE DARK GLORY OF CRIMINALS NOTES ON THE ICONIC IMAGINATION OF THE MULTITUDES

This article explores the relationships between crime, collective responses to it, and the social production of so-called great criminals. It argues that crime, especially sexual and violent crime, produces significant imbalances in individuals habitually subject to instrumental actions, identitarian thinking and positive law. These imbalances are emotional as well as cognitive and, under certain conditions of communication, can generate states of multitude, that is, collective states linked to an intense affectivity and to the prevalence of mythic or symbolic thinking. These states reach their limits and become condensed in the mytho-historical figure of the great criminal. In this sense, great criminals are a function of such multitudinous states: points of imputation that concentrate and catalyze the affective imagination unleashed by collective effervescence

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