Forecasting spatio‑temporal variation in residential burglary with the integrated Laplace approximation framework: Effects of crime generators, street networks, and prior crimes

Objectives - We investigate the spatio-temporal variation of monthly residential burglary frequencies across neighborhoods as a function of crime generators, street network features and temporally and spatially lagged burglary frequencies. In addition, we evaluate the per-formance of the model as a forecasting tool. Methods - We analyze 48 months of police-recorded residential burglaries across 20 neigh-borhoods in Amsterdam, the Netherlands, in combination with data on the locations of urban facilities (crime generators), frequencies of other crime types, and street network data. We apply the Integrated Laplace Approximation method, a Bayesian forecasting framework that is less computationally demanding than prior frameworks. Results - The local number of retail stores, the number of street robberies perpetrated and the closeness of the local street network are positively related to residential burglary. Inclu-sion of a general spatio-temporal interaction component significantly improves forecasting performance, but inclusion of spatial proximity or temporal recency components does not.DiscussionOur findings on crime generators and street network characteristics support evi-dence in the literature on environmental correlates of burglary. The significance of spatio-temporal interaction indicates that residential burglary is spatio-temporally concentrated. Our finding that recency and proximity of prior burglaries do not contribute to the perfor-mance of the forecast, probably indicates that relevant spatio-temporal interaction is lim-ited to fine-grained spatial and temporal units of analysis, such as days and street blocks. Discussion - Our findings on crime generators and street network characteristics support evidence in the literature on environmental correlates of burglary. The significance of spatio-temporal interaction indicates that residential burglary is spatio-temporally concentrated. Our finding that recency and proximity of prior burglaries do not contribute to the performance of the forecast, probably indicates that relevant spatio-temporal interaction is limited to fine-grained spatial and temporal units of analysis, such as days and street blocks

Inequality in exposure to crime, social disorganisation and collective efficacy: Evidence from Greater Manchester, United Kingdom

This paper assesses the relevance of social disorganisation and collective efficacy in accounting for neighbourhood inequalities in the exposure to crime. Specifically, it questions the potential of community and voluntary organisations to enhance informal social control and reduce exposure to crime. It utilises calls-for-service (incident) data for Greater Manchester (UK) and a Bayesian spatio-temporal modelling approach. Contrary to expectations, the research finds that measures of social disorganisation (concentrated disadvantage aside) and collective efficacy hold a limited effect on neighbourhood exposure to crime. We discuss the implications of these findings for criminological inquiry and theoretical development, highlighting the necessity of such endeavour to account for the national political-economy and welfare regime of research settings

A Natural Experiment in Cheongju

학위논문(석사)--서울대학교 대학원 :보건대학원 보건학과,2019. 8. 황승식.Objectives: The community water fluoridation (referred to as CWF) was conducted in Cheongju City in South Korea from 1982 to 2004. The purpose of this study was to evaluate epidemiologically the risk of CWF for adverse health effect, specifically bone related diseases (hip fracture, osteoporosis, and bone cancer). Design: This study was an ecological study based on natural experiment design. Methods: Study participants were residents in Cheongju from 2004 to 2013 and data were collected by National Health Insurance Service database. Hip fracture, osteoporosis, and bone cancer among adverse health diseases were selected. We ensured the trend of medical use trend after CWF ceased in Cheongju and analyzed the prevalence of selected disease to evaluate the risk of CWF. The Hierarchical Bayesian spatio-temporal Poisson regression model which consider spatial and temporal correlation was performed to analyze the association between implementation of CWF and the prevalence of selected diseases of residents in Cheongju. Conditional autocorrelation (CAR) which is frequently used to control spatial correlation was applied in this analysis. The calculation method for Bayesian estimation was based on the R-INLA. Results: After CWF ceased in Cheongju, we observed increasing trend in hip fracture, osteoporosis and bone cancer in both areas (fluoridated and non-fluoridated areas). However, there was no statically significant difference in the prevalence of selected bone diseases in CWF area (RR = 0.95, 95% CrI: 0.87-1.05; RR = 0.94, 95% CrI: 0.87-1.02; RR = 1.20 95% CrI: 0.89-1.61, respectively). Conclusions: We used a spatiotemporal method to analyze the medical use of selected bone diseases from 2004 to 2013 in Cheongju with small area unit by using National Health Insurance Service data. Our study verified that there was no statistically different in prevalence of selected bone disease between CWF and non-CWF areas after CWF was ceased. With this results, we confirmed that fluoridation has no negative impacts on adverse health effects. There was no clear evidence that exposure of CWF increased the risk on health effects. Our study provided one of the scientific evidence and it is necessary to research and develop as a public health prevention program continuously.Abstract i CHAPTER 1. INTRODUCTION 1 1.1 Background 1 1.2 Literature review 6 1.3 Study objective 15 CHAPTER 2. METHODS 16 2.1 Study design, and setting 16 2.2 Data descriptions and study subjects 19 2.3 Variables 20 2.4 Statistical analysis 23 CHAPTER 3. RESULTS 33 3.1 General characteristics of study population 33 3.2 Comparison of crude and age-standardized rates 36 3.3 Comparison of the relative risk of selected diseases 39 3.4 Disease mapping of selected diseases 42 3.5 Comparison of the performance of the models 45 CHAPTER 4. DISCUSSION 48 4.1 Summary of results: A new finding of this study 48 4.2 Comparison with previous studies 48 4.3 Strengths and Limitations of this study 49 4.4 Public health implications 51 CHAPTER 5. CONCLUSION 52 BIBLIOGRAPHY 53 APPENDIX A: Summary of systematic review results 59 APPENDIX B: R-INLA coding 66 APPENDIX C: CARBayesST coding 67 APPENDIX D: Compare the relative risk 68 APPENDIX E: Disease mapping with three different regions 69 APPENDIX F: Outline of the methods 71 APPENDIX G: RECORD statement 72 Abstract (Korean) 79Maste

Spatial dependence of body mass index and exposure to night-time noise in the Geneva urban area

In this study, we calculated the night-noise mean (SonBase 2014, compatible with the EU Environmental Noise Directive) for the 5 classes obtained after computation of Local Indicators of Spatial Association (LISA; Anselin et al 1995) on the BMI of the participants in the Bus Santé study, a cohort managed by the Geneva University Hospitals (N=15’544; Guessous et al 2014). We expected the mean of dBs to be significantly higher in the group showing spatial dependence of high BMI values (high-high class). We ran an ANOVA and multiple T-tests to compare the dB means between LISA clusters. The approach was applied to the participants of the whole State Geneva cohort, and to a reduced set of individuals living in the urban environment of the municipality of Geneva only

The spatial patterning of emergency demand for police services: a scoping review

This preregistered scoping review provides an account of studies which have examined the spatial patterning of emergency reactive police demand (ERPD) as measured by calls for service data. To date, the field has generated a wealth of information about the geographic concentration of calls for service, but the information remains unsynthesised and inaccessible to researchers and practitioners. We code our literature sample (N = 79) according to the types of demand studied, the spatial scales used, the theories adopted, the methods deployed and the findings reported. We find that most studies focus on crime-related call types using meso-level (e.g., neighborhood) spatial scales. Descriptive methods demonstrate the non-random distribution of calls, irrespective of their type, while correlational findings are mixed, providing minimal support for theories such as social disorganization theory. We conclude with suggestions for future research, focusing on how the field can better exploit open data sources to ‘scale-up’ analyses

Características de los vecindarios y la distribución espacial de problemas sociales en la ciudad de Valencia

The aim of this doctoral thesis is to explore the influence of neighborhood-level variables on the spatial and spatio-temporal distribution of different social problems in the city of Valencia. In Study 1, we present data on the development and validation of an observational instrument to assess neighborhood disorder. Results supported a three-factor model (physical disorder, social disorder and physical deterioration), and they showed good reliability and validity evidences. In Study 2, we assess the psychometric properties of a neighborhood disorder scale using Google Street View. Results supported a bifactorial solution with a general factor (general neighborhood disorder) and two specific factors (physical disorder and physical decay), and also showed good indicators of reliability and validity. In Study 3, we analyze the spatial distribution of drug-related police interventions and the neighborhood characteristics influencing these spatial patterns. Results indicated that high physical decay, low socioeconomic status, and high immigrant concentration were associated with high levels of drug-related police interventions. In Study 4, we analyze the spatio-temporal distribution of alcohol outlet density and its relationship with neighborhood characteristics. Results showed that off-premise density was higher in areas with lower economic status, higher immigrant concentration, and lower residential instability; restaurant and cafe density was higher in areas with higher spatially-lagged economic status, and bar density was higher in areas with higher economic status and higher spatially-lagged economic status. Furthermore, restaurant and cafe density was negatively associated with alcohol-related police calls-for-service, while bar density was positively associated with alcohol-related calls-for-service. In Study 5, we analyze the spatio-temporal distribution of suicide-related emergency calls. Results showed the importance of using a spatio-temporal modeling that also includes a seasonality effect. In Study 6, we analyze the relationship of suicide-related calls with neighborhood-level variables. Results showed that neighborhoods with lower levels of education level and population density, and higher levels of residential instability, percentage of one-person households and aging population had higher levels of suicide-related calls for service. Finally, in Study 7, we analyze the influence of university campuses on intimate partner violence against women risk. Results showed that the distance to the university campuses was associated with an increased risk of intimate partner violence against women, once controlled for other types of neighborhood-level variables. This doctoral thesis contributes to the understanding of the neighborhood-level characteristics associated with different social problems. These results are useful when planning and implementing community-level prevention and intervention strategies

Spatial and spatio-temporal variability in social, emotional and behavioural development of children

Neighbourhood differences in early development can be explored by incorporating spatial and spatio-temporal information with population data. Spatial refers to the relationship between neighbouring areas, while spatio-temporal refers to the relationship between neighbouring areas over time. At the time of writing, most population studies have focused on spatial variation in early development over a single year or short time period. This project identifies spatial and spatio-temporal referenced data that can be linked with population data on child social, emotional and behavioural difficulties in Glasgow, United Kingdom (UK). The Child Mental Health in Education (ChiME) study is a unique resource that can be used to model long term trends in a preschool population. In the ChiME study, Strengths and Difficulties Questionnaire (SDQ) forms were analysed for 35,171 children aged 4–5 years old across 180 preschools in Glasgow, UK, between 2010 and 2017 as part of routine monitoring. Using ChiME data, this work examined how early development varies over space and time, how the neighbourhood is defined, how important the neighbourhood is and what neighbourhood characteristics are related to early development. A literature review of 71 studies (from 2012-2022) in Chapter 2 discusses the neighbourhood constructs that are associated with variation in early development for children in Scotland. These constructs included the physical environment (e.g. greenspace) and social environment (e.g. social networks). The availability of data and the strength of evidence to support each construct varied. For many constructs, there was limited understanding of their relevance to younger children as opposed to adolescent or adult populations. There are gaps in the literature in the extent that neighbourhood constructs relate to developmental outcomes at an individual level or how this may change over time. To address these gaps, much more multilevel research, using population data is required. Chapter 3 provides a methodological review of the multilevel spatio-temporal approaches used to date. There is limited methodological guidance on how to model spatio-temporal variation for multilevel data. There is a risk of over complicating the model when attempting to account for spatial, temporal and/or spatio-temporal effects. Choosing the appropriate spatio-temporal multilevel model depends on the structure of the data, the degree of correlation, the goal of the analysis and overall model fit. Using a Bayesian workflow, each component of the model is reviewed in an iterative process to provide the best model for the data in Chapter 4. This includes evaluation of the outcome (total difficulties scores vs high scores) and comparing discrete distributions (Poisson, Negative Binomial and Zero-Inflated Negative Binomial models). Workflow analysis supported the use of Zero Inflated Negative Binomial distribution for total difficulties scores and the use of approximation methods for estimation. The total difficulties score for an individual child nested in their preschool, electoral ward and ward:year was modelled using a multilevel model with the components selected in Chapter 4. In Chapter 5, models were built incrementally, considering the value of each context. Boys, those of increasing deprivation and children outside the average age, had more difficulties on average. Preschool and ward variation, although minimal, highlight potential priority areas for local service provision. After consideration of demographics (sex, age, and deprivation), the overall spatial effect found the electoral wards of Anderston, Craigton, North East and Pollokshields were worse than expected (Relative Risk > 1) from 2010 to 2017. There were 72 preschools that were worse than expected based on their demographics. Approximately half of the children who lived in a ward that was worse than expected also attended a preschool that was worse than expected. There were independent spatio-temporal patterns in total difficulties, that exist in addition to the overall spatial effect. Spatial effects were not solely due to consistently poor performing areas. Instead, there is evidence of yearly variations in performance. Spatial analysis using only a single or few years may lead to misleading conclusions about area level variability. For example, once considering the spatio-temporal effect, Pollokshields was no longer considered worse than expected. There were differences in spatial and spatio-temporal variation depending on the neighbourhood definition (electoral ward, locality, Intermediate Zone (2001 and 2011) and Consistent Areas Through Time (CATTs)) found in Chapter 6. Looking at the different spatial scales together, can help support diffuse or more concentrated intervention delivery. Localities and 2011 Intermediate Zones had a similar spatial distribution to the ward. The relative importance of the neighbourhood compared to other contexts can be quantified through the Variance Partition Coefficient (VPC). Estimated VPC of the neighbourhood on early development was expected to be between 0 and 9% according to recent literature. Though the typical VPC equation does not apply to discrete distributions, recent approximations have been developed. Using these approximations, it was found that proportionally, the neighbourhood context alone does not make a considerable contribution to variation in difficulties scores. VPC values ranged from 0.39-1.1% depending on the neighbourhood definition. From the perspective of decision-making, the partitioned variance suggests that considering the neighbourhood along with other contexts would be more meaningful than the neighbourhood alone. Preschool and neighbourhood characteristics are thought to provide a more feasible target for intervention compared to individual level characteristics. Cross-level effects (which describe the association between a higher level covariate and a lower level outcome) are investigated in Chapter 7. Preschool and neighbourhood indicators were derived from openly available administrative data. The quality of these indicators and their relevance to this project varied. Preschools were classified as small/medium/large local authority, private business or voluntary. Most children were in local authority preschools. Total difficulties scores were lower in private business compared to small local authority preschools. Spatial variation was in part explained by a child’s prosocial behaviour and its interaction with their preschool provider. The mechanisms underlying these differences are at present unknown. There were ecological correlations between total difficulties and the neighbourhood indicators (participation, child poverty, domestic abuse, free time places, vandalism and proximity to greenspace (at 400 m and 800m)). These correlations did not translate to a cross-level association with individual level total difficulties. In conclusion, there are multiple contexts that account for variation in total difficulties. The preschool and spatio-temporal context and their composition could provide additional information about how the neighbourhood relates to early development. There is a need for more spatio-temporal data, that can be linked to population data, to understand how the neighbourhood is associated with development at an individual level, beyond deprivation. Multi-level spatio-temporal models can be used to understand early development and support the selection of delivery areas for place-based interventions.Neighbourhood differences in early development can be explored by incorporating spatial and spatio-temporal information with population data. Spatial refers to the relationship between neighbouring areas, while spatio-temporal refers to the relationship between neighbouring areas over time. At the time of writing, most population studies have focused on spatial variation in early development over a single year or short time period. This project identifies spatial and spatio-temporal referenced data that can be linked with population data on child social, emotional and behavioural difficulties in Glasgow, United Kingdom (UK). The Child Mental Health in Education (ChiME) study is a unique resource that can be used to model long term trends in a preschool population. In the ChiME study, Strengths and Difficulties Questionnaire (SDQ) forms were analysed for 35,171 children aged 4–5 years old across 180 preschools in Glasgow, UK, between 2010 and 2017 as part of routine monitoring. Using ChiME data, this work examined how early development varies over space and time, how the neighbourhood is defined, how important the neighbourhood is and what neighbourhood characteristics are related to early development. A literature review of 71 studies (from 2012-2022) in Chapter 2 discusses the neighbourhood constructs that are associated with variation in early development for children in Scotland. These constructs included the physical environment (e.g. greenspace) and social environment (e.g. social networks). The availability of data and the strength of evidence to support each construct varied. For many constructs, there was limited understanding of their relevance to younger children as opposed to adolescent or adult populations. There are gaps in the literature in the extent that neighbourhood constructs relate to developmental outcomes at an individual level or how this may change over time. To address these gaps, much more multilevel research, using population data is required. Chapter 3 provides a methodological review of the multilevel spatio-temporal approaches used to date. There is limited methodological guidance on how to model spatio-temporal variation for multilevel data. There is a risk of over complicating the model when attempting to account for spatial, temporal and/or spatio-temporal effects. Choosing the appropriate spatio-temporal multilevel model depends on the structure of the data, the degree of correlation, the goal of the analysis and overall model fit. Using a Bayesian workflow, each component of the model is reviewed in an iterative process to provide the best model for the data in Chapter 4. This includes evaluation of the outcome (total difficulties scores vs high scores) and comparing discrete distributions (Poisson, Negative Binomial and Zero-Inflated Negative Binomial models). Workflow analysis supported the use of Zero Inflated Negative Binomial distribution for total difficulties scores and the use of approximation methods for estimation. The total difficulties score for an individual child nested in their preschool, electoral ward and ward:year was modelled using a multilevel model with the components selected in Chapter 4. In Chapter 5, models were built incrementally, considering the value of each context. Boys, those of increasing deprivation and children outside the average age, had more difficulties on average. Preschool and ward variation, although minimal, highlight potential priority areas for local service provision. After consideration of demographics (sex, age, and deprivation), the overall spatial effect found the electoral wards of Anderston, Craigton, North East and Pollokshields were worse than expected (Relative Risk > 1) from 2010 to 2017. There were 72 preschools that were worse than expected based on their demographics. Approximately half of the children who lived in a ward that was worse than expected also attended a preschool that was worse than expected. There were independent spatio-temporal patterns in total difficulties, that exist in addition to the overall spatial effect. Spatial effects were not solely due to consistently poor performing areas. Instead, there is evidence of yearly variations in performance. Spatial analysis using only a single or few years may lead to misleading conclusions about area level variability. For example, once considering the spatio-temporal effect, Pollokshields was no longer considered worse than expected. There were differences in spatial and spatio-temporal variation depending on the neighbourhood definition (electoral ward, locality, Intermediate Zone (2001 and 2011) and Consistent Areas Through Time (CATTs)) found in Chapter 6. Looking at the different spatial scales together, can help support diffuse or more concentrated intervention delivery. Localities and 2011 Intermediate Zones had a similar spatial distribution to the ward. The relative importance of the neighbourhood compared to other contexts can be quantified through the Variance Partition Coefficient (VPC). Estimated VPC of the neighbourhood on early development was expected to be between 0 and 9% according to recent literature. Though the typical VPC equation does not apply to discrete distributions, recent approximations have been developed. Using these approximations, it was found that proportionally, the neighbourhood context alone does not make a considerable contribution to variation in difficulties scores. VPC values ranged from 0.39-1.1% depending on the neighbourhood definition. From the perspective of decision-making, the partitioned variance suggests that considering the neighbourhood along with other contexts would be more meaningful than the neighbourhood alone. Preschool and neighbourhood characteristics are thought to provide a more feasible target for intervention compared to individual level characteristics. Cross-level effects (which describe the association between a higher level covariate and a lower level outcome) are investigated in Chapter 7. Preschool and neighbourhood indicators were derived from openly available administrative data. The quality of these indicators and their relevance to this project varied. Preschools were classified as small/medium/large local authority, private business or voluntary. Most children were in local authority preschools. Total difficulties scores were lower in private business compared to small local authority preschools. Spatial variation was in part explained by a child’s prosocial behaviour and its interaction with their preschool provider. The mechanisms underlying these differences are at present unknown. There were ecological correlations between total difficulties and the neighbourhood indicators (participation, child poverty, domestic abuse, free time places, vandalism and proximity to greenspace (at 400 m and 800m)). These correlations did not translate to a cross-level association with individual level total difficulties. In conclusion, there are multiple contexts that account for variation in total difficulties. The preschool and spatio-temporal context and their composition could provide additional information about how the neighbourhood relates to early development. There is a need for more spatio-temporal data, that can be linked to population data, to understand how the neighbourhood is associated with development at an individual level, beyond deprivation. Multi-level spatio-temporal models can be used to understand early development and support the selection of delivery areas for place-based interventions