Dissertations / Theses on the topic 'Crime forecasting'

У роботі розглядаються теоретичні та методологічні питання щодо статистичної оцінки правопорушень та злочинності в Україні. Проаналізовано динаміку рівня злочинності в Україні та проаналізована структура злочинів. Проведено аналіз регіональних особливостей розповсюдженості злочинності в Україні. Проведено аналіз тенденції розвитку та рівня злочинності в Україні.
The paper considers theoretical and methodological issues related to statistical assessment of offenses and crime in Ukraine. The dynamics of the crime rate in Ukraine is analyzed and the structure of crimes is analyzed. The analysis of regional features of crime prevalence in Ukraine is carried out. An analysis of development trends and crime rates in Ukraine.

The low amount solved residential burglary crimes calls for new and innovative methods in the prevention and investigation of the cases. There were 22 600 reported residential burglaries in Sweden 2017 but only four to five percent of these will ever be solved. There are many initiatives in both Sweden and abroad for decreasing the amount of occurring residential burglaries and one of the areas that are being tested is the use of prediction methods for more efficient preventive actions. This thesis is an investigation of a potential method of prediction by using neural networks to identify areas that have a higher risk of burglaries on a daily basis. The model use reported burglaries to learn patterns in both space and time. The rationale for the existence of patterns is based on near repeat theories in criminology which states that after a burglary both the burgled victim and an area around that victim has an increased risk of additional burglaries. The work has been conducted in cooperation with the Swedish Police authority. The machine learning is implemented with convolutional long short-term memory (LSTM) neural networks with max pooling in three dimensions that learn from ten years of residential burglary data (2007-2016) in a study area in Stockholm, Sweden. The model's accuracy is measured by performing predictions of burglaries during 2017 on a daily basis. It classifies cells in a 36x36 grid with 600 meter square grid cells as areas with elevated risk or not. By classifying 4% of all grid cells during the year as risk areas, 43% of all burglaries are correctly predicted. The performance of the model could potentially be improved by further configuration of the parameters of the neural network, along with a use of more data with factors that are correlated to burglaries, for instance weather. Consequently, further work in these areas could increase the accuracy. The conclusion is that neural networks or machine learning in general could be a powerful and innovative tool for the Swedish Police authority to predict and moreover prevent certain crime. This thesis serves as a first prototype of how such a system could be implemented and used.

The thesis, entitled "Essays on macroeconometrics and short-term forecasting",

is composed of three chapters. The first two chapters are on nowcasting,

a topic that has received an increasing attention both among practitioners and

the academics especially in conjunction and in the aftermath of the 2008-2009

economic crisis. At the heart of the two chapters is the idea of exploiting the

information from data published at a higher frequency for obtaining early estimates

of the macroeconomic variable of interest. The models used to compute

the nowcasts are dynamic models conceived for handling in an efficient way

the characteristics of the data used in a real-time context, like the fact that due to the different frequencies and the non-synchronicity of the releases

the time series have in general missing data at the end of the sample. While

the first chapter uses a small model like a VAR for nowcasting Italian GDP,

the second one makes use of a dynamic factor model, more suitable to handle

medium-large data sets, for providing early estimates of the employment in

the euro area. The third chapter develops a topic only marginally touched

by the second chapter, i.e. the estimation of dynamic factor models on data characterized by block-structures.

The firrst chapter assesses the accuracy of the Italian GDP nowcasts based

on a small information set consisting of GDP itself, the industrial production

index and the Economic Sentiment Indicator. The task is carried out by using

real-time vintages of data in an out-of-sample exercise over rolling windows

of data. Beside using real-time data, the real-time setting of the exercise is

also guaranteed by updating the nowcasts according to the historical release calendar. The model used to compute the nowcasts is a mixed-frequency Vector

Autoregressive (VAR) model, cast in state-space form and estimated by

maximum likelihood. The results show that the model can provide quite accurate

early estimates of the Italian GDP growth rates not only with respect

to a naive benchmark but also with respect to a bridge model based on the

same information set and a mixed-frequency VAR with only GDP and the industrial production index.

The chapter also analyzes with some attention the role of the Economic Sentiment

Indicator, and of soft information in general. The comparison of our

mixed-frequency VAR with one with only GDP and the industrial production

index clearly shows that using soft information helps obtaining more accurate

early estimates. Evidence is also found that the advantage from using soft

information goes beyond its timeliness.

In the second chapter we focus on nowcasting the quarterly national account

employment of the euro area making use of both country-specific and

area wide information. The relevance of anticipating Eurostat estimates of

employment rests on the fact that, despite it represents an important macroeconomic

variable, euro area employment is measured at a relatively low frequency

(quarterly) and published with a considerable delay (approximately

two months and a half). Obtaining an early estimate of this variable is possible

thanks to the fact that several Member States publish employment data and

employment-related statistics in advance with respect to the Eurostat release

of the euro area employment. Data availability represents, nevertheless, a

major limit as country-level time series are in general non homogeneous, have

different starting periods and, in some cases, are very short. We construct a

data set of monthly and quarterly time series consisting of both aggregate and

country-level data on Quarterly National Account employment, employment

expectations from business surveys and Labour Force Survey employment and

unemployment. In order to perform a real time out-of-sample exercise simulating

the (pseudo) real-time availability of the data, we construct an artificial

calendar of data releases based on the effective calendar observed during the first quarter of 2012. The model used to compute the nowcasts is a dynamic

factor model allowing for mixed-frequency data, missing data at the beginning

of the sample and ragged edges typical of non synchronous data releases. Our

results show that using country-specific information as soon as it is available

allows to obtain reasonably accurate estimates of the employment of the euro

area about fifteen days before the end of the quarter.

We also look at the nowcasts of employment of the four largest Member

States. We find that (with the exception of France) augmenting the dynamic

factor model with country-specific factors provides better results than those

obtained with the model without country-specific factors.

The third chapter of the thesis deals with dynamic factor models on data

characterized by local cross-correlation due to the presence of block-structures.

The latter is modeled by introducing block-specific factors, i.e. factors that

are specific to blocks of time series. We propose an algorithm to estimate the model by (quasi) maximum likelihood and use it to run Monte Carlo

simulations to evaluate the effects of modeling or not the block-structure on

the estimates of common factors. We find two main results: first, that in finite samples modeling the block-structure, beside being interesting per se, can help

reducing the model miss-specification and getting more accurate estimates

of the common factors; second, that imposing a wrong block-structure or

imposing a block-structure when it is not present does not have negative

effects on the estimates of the common factors. These two results allow us

to conclude that it is always recommendable to model the block-structure

especially if the characteristics of the data suggest that there is one.
Doctorat en Sciences économiques et de gestion
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Este trabalho busca analisar empiricamente a existência de bolhas imobiliárias ao redor do mundo e identificar quando esses comportamentos explosivos no preço dos imóveis ocorreram. Os resultados foram obtidos por meio de uma metodologia recursiva de testes de raiz unitária, os testes SADF e GSADF propostos por Phillips e co-autores. Foram coletados dados de preços de imóveis para 28 países e seus respectivos índices de preços ao consumidor. Os resultados obtidos apontaram a existência de comportamentos explosivos em aproximadamente 90% das séries analisadas.
This study aims to empirically analyze the existence of real estate bubbles around the world and to identify when these explosive behavior in real estate prices occurred. The results were obtained through a recursive methodology of unit root tests, the SADF and GSADF tests proposed by Phillips and co-authors. Real estate price data were collected for 28 countries and their respective consumer price indexes. The results obtained indicate the existence of explosive behavior in about 90% of the analyzed series.

Internship Report presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
In recent years, there have been many discussions of data mining technology implementation in the fight against terrorism and crime. Sentient as a software company has been supporting the police for years by applying data mining techniques in the DataDetective application (Sentient, 2017). Experimenting with various types of predictive model solutions, selecting the most efficient and promising solution are the objectives of this internship. Initially, extended literatures were reviewed in the field of data mining, crime analysis and crime data mining. Sentient provided 7 years of crime data which was aggregated on daily basis to create a univariate dataset. Also, an incidence type daily aggregation was done to create a multivariate dataset. The prediction length for each solution was 7 days. The experiments were divided into two major categories: Statistical models and neural network models. Neural networks outperformed statistical models for the crime data. This paper provides the overview of statistical models and neural network models used. A comparative study of all the models on similar dataset gives a clear picture of their performance on available data and generalization capability. Evidently, the experiments showed that Gated Recurrent units (GRU) produced better prediction in comparison to other models. In conclusion, gated recurrent unit implementation could give benefit to police in predicting crime. Hence, time series analysis using GRU could be a prospective additional feature in DataDetective.

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.

The problem investigated in this research is the ongoing crime threat and the extreme risks which impact negatively on the sustainability of the Rosslyn Industry - the industrial hub of Tshwane in the Gauteng Provence of South Africa. Businesses in Rosslyn are desperate for a solution that will mitigate these crime threats and risks, and ensure the future sustainability of this important industrial community. An intervention model is urgently required to prevent this type of crime, not only as a short term solution but as a sustainable long term intervention. This research study initiated the collaboration required for the successful implementation of a Crime Prevention Intervention Model (CPIM) in the Rosslyn industrial environment. The intended crime prevention model has been designed in such a way that it addresses the entire environment of crime that prevails in the Rosslyn area involving both the offender and the victim. This design is rooted in the ontology of Environmental Criminology and more specific on the applied epistemology of Crime Prevention Through Environmental Design (CPTED). Participants in this project are representatives who are responsible for all security functions in both big businesses and small enterprises. And with their dedicated assistance the research findings disclosed the current crime status of the Rosslyn environment regarding the threat, risk, security vulnerabilities, controls and needs: • Crime and its causal factors, in Rosslyn, are rife and no noteworthy action has been implemented to mitigate these threats. • Collaboration between Rosslyn role players (neighbours, local government and law enforcement) is for all purposes non-existent. • And to complicate matters even more, knowledge of how to effectively mitigate crime is limited and handicapped by the re-active physical security methods currently being used. • The implication of these findings is that the status quo will eventually render business in Rosslyn unsustainable. Thus a CPIM in Rosslyn is inevitable. What was crucial to this research and to the CTPED design is the detailed sourcing of accurate data addressing the experiences and the needs the respondents identified in the current Rosslyn crime situation concerning; status, the threat, risk, security, vulnerabilities and controls. In order to achieve this level of data sourcing and assimilation, the essential features of the research method were based on a mixed approach where quantitative and qualitative methods were implemented in parallel. The diverse fields, sources and respondent mix required for a Rosslyn Industry CPIM also necessitated a MIT (Multi,-Inter,-Trans,-Disciplinary) approach. This MIT requirement is successfully facilitated through the applied criminological CPTED approach. The CPIM is based on the combined outcomes of the following three research fields: • Field-one: Environmental criminology theories are researched through an in-depth literature review to demonstrate the criminological grounding of crime prevention and to guide its application through the development of an applied CPTED SUITE. • Field-two: Supply Chain Security (SCS) are researched through an in-depth literature review to establish its criminological relevance and applications. SCS requirements are identified and built into the Field-Three research process and tested for relevance and for incorporation in the CPTED SUITE. • Field-three: Based on a mixed research process, using a custom designed Criminological Risk Analyses tool incorporating scheduled interviews and questionnaires, the crime and needs profile of the Rosslyn Industry are uncovered and analysed. The results are filtered through the CPTED SUITE to indicate the correct criminological approach for mitigating the identified problems and needs. Even though this study takes an applied crime preventative approach, the criminological-philosophical mould of crime prevention is imperative for the effective application of the CPTED. Security and crime prevention training, planning and application, without this approach will remain underdeveloped and outdated. Finally the underlying intention of this research is for this Crime Prevention Intervention Model (CPIM) to be adapted and implemented and to serve as a guide or a benchmark for security practitioners in any industrial environment that has the same crime threats and crime risk challenges.
Criminology and Security Science
D. Litt. et Phil. (Criminology)

My thesis deals with the very complicated topic of juvenile crime. The circumstances that result in socially pathological phenomenons and crime are now-days relatively easy to happen. The questions of juvenile crime are more frequently discussed not only by the scientists but also by the general public. The goal of my thesis is to outline the problems of the juvenile crime development in the district Klatovy and to use the acquired information in crime prevention and to focus the care on difficult individuals. In the theoretical part, the thesis presents the development of juvenile crime in the Czech Republic and factors that shape and influence it. The practical part of my thesis describes the performed research that is a type of quantitative research from the sociological point of view. The method of questioning, the method of data secondary analysis and the method of model casuistics were used. The targeted group is represented by children and adolescents living in the district Klatovy. The respondents´ principal residence in Klatovy district and the age under 18 are the main criteria of categorization. All respondents were chosen with the method of random stratification. The research was held from January to March of the year 2009. Following hypothesis were determined: Hypothesis 1: The rate of juvenile crime is higher in towns than in villages. Hypothesis 2: Socially pathological features are side-effects of the juvenile crime. Hypothesis 3: Child crime in the district Klatovy is ascending in comparison to adolescent crime in the district Klatovy. I believe that all three hypotheses were confirmed. Based on the OSPOD (Department for juvenile care) reports we can assume that there is na increase in educational problems and subsequently the crime is gradually increasing. If we consider the crime committed by children in the district Klatovy from 2004 to 2008, we find that the year 2007 is a year with the lowest crime rate monitored. We are witnessing the gradually decreasing development trend. Lately, the total development of child crime has been slightly increasing and adolescent crime slowly decreasing.

A previsão de séries financeiras faz parte do processo de decisão das políticas monetárias por parte dos bancos centrais. Mendes, Ferreira e Mendes (2020) propõem um modelo híbrido que junta um VECM (modelo vetorial corretor de erro) com um algoritmo de aprendizagem profunda o LSTM (memória de longo curto-prazo) para uma previsão multivariada do índice acionista norte-americano S&P500, utilizando-se as séries do Nasdaq, Dow Jones e as taxas de juro dos bilhetes do tesouro americano a 3 meses no mercado secundário, com dados semanais, entre 19/04/2019 e 17/04/2020. Nesta dissertação, replicou-se esse artigo e construiu-se um modelo híbrido semelhante com a mesma finalidade e obteve-se um erro de previsão MAPE 86% inferior (4% versus 28%), mesmo incluindo a crise da COVID-19. Analisou-se o período sem crise e obteve-se um MAPE de 1.9%. Verificou-se que o vazamento de dados entre os períodos de teste e treino é um problema que prejudica os resultados. Comparou-se diferentes formas de construir o modelo híbrido variando o número de desfasamentos e de épocas de treino no LSTM, verificou-se o impacto de logaritmizar as séries, e comparou-se com modelos de referência (LSTM univariado/multivariado). Além disso, testou-se a causalidade à Granger entre os períodos com forte intervenção por parte da FED (décadas de 70 e 80, e crise da COVID-19 em fevereiro de 2020), concluindo-se que a variação das taxas de juro causam à Granger os retornos dos índices acionistas analisados, invertendo-se essa relação causal fora desses períodos.
The forecasting of financial series is part of the decision-making process of monetary policies by central banks. Mendes, Ferreira and Mendes (2020) proposed a hybrid model that combines a VECM (Vector Error Correction Model) with a deep learning algorithm LSTM (Long Short-Term Memory) for a multivariate forecast of the U.S. stock index S&P500, using Nasdaq, Dow Jones and U.S. treasury bills for 3 months yields of the secondary market series, with weekly data, between 19/04/2019 and 17/04/2020. In this dissertation, this article was replicated, and a similar hybrid model was constructed with the same purpose and an 86% lower MAPE forecast error was obtained (4% versus 28%), even including the COVID-19 crisis. The time period without the crisis was analyzed and a MAPE of 1.9% was obtained. It was found that data leakage between the test and training periods is a problem that impairs the results. Different ways of constructing the hybrid model were compared by varying the number of lags and training epochs in LSTM, the impact of using the log-series was verified, and benchmarking with univariate and multivariate LSTM was made. In addition, granger causality was tested between the time periods with strong intervention by the FED (1970s and 1980s, and the COVID-19 crisis in February 2020) concluding that the changes in yields Granger cause the stock indices returns. In contrast, this causal relationship outside these time periods was the opposite, with the indices returns causing the changes in yields.

Sexual abuse of children occurs at a very high rate and most of the victims are young children who have never been taught about the possibility of being abused. Victims of abuse are not equipped with appropriate knowledge and vocabulary to enable them to explain properly when they experience abuse. Acquisition of a vocabulary and understanding of the concepts of sexuality would assist children in recognizing, resisting and reporting sexual abuse. The young age at which abuse occurs makes the study at primary school level important and relevant. There is little literature on how child sexual abuse can be identified, addressed and handled by schools or what unique role the school should play in the management of child sexual abuse cases. Educators are in a position to identify sexually abused children because of their close and ongoing contact with school-going children. Therefore, young children place a great deal of trust in their teachers and look to them for protection when they feel unsafe. Teachers are trained to observe changes in the appearance and progress of individual children. Therefore, they can also assist in uncovering and reacting appropriately to disclosures of abuse. The study was conducted in poor, disadvantaged, rural primary schools in a part of the North West Province. There is a limited access to social work services in rural areas and therefore, educators are considered to be appropriate people to educate children about sexual abuse issues because children spend most of their time at school. There is a relationship between educators, parents and children which creates a proper channel of communicating information about sexual abuse of children. That is, educators are in a position not only to educate but also to reinforce what children have learnt at home. Educators' role have been limited in the identification and dealing with sexual abuse cases and therefore, there is a need to empower them with appropriate knowledge and skills to enable them to handle sexual abuse cases at school level successfully.
Psychology
M. A. (Psychology)