Exploring offence statistics in stockholm city using spatial analysis tools

ABSTRACT The objective of this paper is to investigate changes in offence patterns in Stockholm City using methods from spatial statistics. The paper has two parts. The first is a brief description of methodological procedures to obtain robust geographical units for spatial statistical analysis. The second part focuses on a discussion of the results of different types of spatial statistical analyses of offence patterns for Stockholm City. Standardised offence rates (SOR) are calculated and mapped using GIS for three offences: residential burglary, theft of and from cars and vandalism. The Getis-Ord statistic is used to identify crime clusters or hot spots and finally offence patterns are analysed as a function of socio-economic variables using the linear regression model. The findings of previous Swedish studies on crime patterns, mostly by Wikström (1991), and the insights provided by North American and British theories on crime patterns provide a background for this study. Results suggest that whilst there have been no dramatic changes in the geographies of these offences in Stockholm City during the last decade, there have been some shifts both in terms of geographical patterns and in their association with underlying socio-economic conditions.

The Effects of Subdivision Design on Housing Values: The Case of Alleyways

Subdivision design likely impacts residential housing values. This study examines the sale prices of houses located in subdivisions utilizing rear-entry alleyways in the Greater Dallas-Fort Worth-Denton metroplex. Regression analysis on a sample of 1,672 home sales, some of which are located on alleyways, reveals statistically significant impacts. Consequently, developers, appraisers, New Urbanists and other real estate participants should consider subdivision design when estimating value for residential dwellings.

Expert Opinion versus Transaction Evidence: Using the Reilly Index to Measure Open Space premiums in the Urban-Rural Fringe

Due to economic and population growth farmland and to a lesser extend other undeveloped areas are under pressure in the urban-rural fringe in British Columbia, Canada. The objectives of this paper are to determine if residential property values near Victoria, BC include open-space premiums for farmland, parks or golf courses, and to determine if using assessed values instead of market prices of the property result in the same findings. We estimate a Seemingly Unrelated Regression (SUR) model with two hedonic pricing equations, one with actual market values as the dependent variable and one with assessed property values, and compare the resulting estimates of shadow prices for open space amenities. Furthermore, we take account of spatial autocorrelation and combine Method of Moment estimates of the spatial parameters in both equations.Hedonic pricing models, spatial dependence, assessed property values, open space.

Does Regulation of Built-In Security Reduce Crime? Evidence From a Natural Experiment

As of 1999, all new-built homes in the Netherlands have to have burglary-proof windows and doors. We provide evidence that this large-scale government intervention in the use of self-protective measures lowers crime and improves social welfare. We find the regulatory change to have reduced burglary in new-built homes from 1.1 to 0.8 percent annually, a reduction of 26 percent. The findings suggest that burglars avoid old, less-protected homes that are located in the direct vicinity of the new, better-protected homes. The presence of a negative externality on older homes is ambiguous. We find no evidence for displacement to other property crimes including theft from cars and bicycle theft. Even though the regulation of built-in security does not target preventative measures at homes that are most at risk, the social benefits of the regulation are likely to exceed the social costs.victim precaution;government regulation;crime

Economic Analyses of Cars in the City:Implications for Policy and Automated Vehicles

This dissertation presents four empirical analyses on the urban economic effects of private vehicles. These analyses contribute to the current debate on urban and transport policy, covering topics ranging from the efficacy of hourly and residential parking prices, to the implications of in-vehicle distractions on road safety, and the long-term impact of cars on urban density. In each case, the empirical estimates are applied to improve our understanding of how automated vehicles (AVs) may impact our cities in the future. In cities, parking occupies a large share of land and is often provided to residents and visitors at prices below the market rate. According to economic theory, this causes excess car ownership and use; however, we lack well-defined quantitative estimates of these effects. Chapter 2 examines the effect of a large citywide increase in hourly on-street parking prices on parking and traffic demand in Amsterdam. Our findings indicate that the citywide increase in hourly on-street parking prices in 2019 of 66%, resulted in 9% fewer on-street parking arrivals and an overall reduction in traffic flow of around 2% - 3%. Chapter 3 focuses specifically on residents and examines how residential parking prices affect car ownership decisions. Our results indicate that for city centres, annual residential parking costs are around €1000, or roughly 17 percent of car ownership costs, and are more than double the costs in the periphery. Households facing one standard deviation (€503) higher annual parking costs own 0.085 fewer cars, corresponding to a price elasticity of car demand of about -0.7. This implies that the disparity in parking costs explains around 30% of the difference in average car ownership rates between the city centre and the periphery. These two chapters support the abundant theoretical literature, which indicates that parking prices are an integral part of urban transport policy. Chapter 4 studies how the rise in smartphone use over the past decade has impacted road safety in the Netherlands. Our results suggest that about 10% of vehicle accidents are caused by smartphone use and that these accidents mainly happen on urban roads. The findings imply that you are about 3.8 times more likely to cause an accident if using a mobile phone, which is larger than earlier field studies performed on data before the prevalence of widespread smartphone adoption. Chapter 5 studies the long-term effect of cars on urban density. Using a global sample of cities, we find that a one standard deviation increase in car ownership rates causes a reduction in population density of around 40%. This effect appears to be driven by expansions in the built-up area, suggesting that cars facilitate lower density development in the periphery. AVs are expected to result in improvements to accessibility and safety, increases in car demand, and a redistribution of people and jobs over space. Chapters 2 and 3 demonstrate how lower parking prices are expected to result in more traffic and vehicle demand within cities. Chapter 4 provides an indication for the potential safety benefits from AVs due to fewer smartphone distractions. Finally, Chapter 5 examines how increases in vehicle access and demand, due to cheaper and more comfortable car travel, are expected to impact urban population density in the long-run

Bus Transit Operational Efficiency Resulting from Passenger Boardings at Park-and-Ride Facilities

In order to save time and money by not driving to an ultimate destination, some urban commuters drive themselves a few miles to specially designated parking lots built for transit customers and located where trains or buses stop. The focus of this paper is the effect Park-and-Ride (P&R) lots have on the efficiency of bus transit as measured in five bus transit systems in the western U.S. This study describes a series of probes with models and data to find objective P&R influence measures that, when combined with other readily-available data, permit a quantitative assessment of the significance of P&R on transit efficiency. The authors developed and describe techniques that examine P&R as an influence on transit boardings at bus stops and on bus boardings along an entire route. The regression results reported are based on the two in-depth case studies for which sufficient data were obtained to examine (using econometric techniques) the effects of park-and-ride availability on bus transit productivity. Both Ordinary Least Square (OLS) regression and Poisson regression are employed. The results from the case studies suggest that availability of parking near bus stops is a stronger influence on transit ridership than residential housing near bus stops. Results also suggest that expanding parking facilities near suburban park-and-ride lots increases the productivity of bus operations as measured by ridership per service hour. The authors also illustrate that reasonable daily parking charges (compared to the cost of driving to much more expensive parking downtown) would provide sufficient capital to build and operate new P&R capacity without subsidy from other revenue sources

Computer vision-enriched discrete choice models, with an application to residential location choice

Visual imagery is indispensable to many multi-attribute decision situations. Examples of such decision situations in travel behaviour research include residential location choices, vehicle choices, tourist destination choices, and various safety-related choices. However, current discrete choice models cannot handle image data and thus cannot incorporate information embedded in images into their representations of choice behaviour. This gap between discrete choice models' capabilities and the real-world behaviour it seeks to model leads to incomplete and, possibly, misleading outcomes. To solve this gap, this study proposes "Computer Vision-enriched Discrete Choice Models" (CV-DCMs). CV-DCMs can handle choice tasks involving numeric attributes and images by integrating computer vision and traditional discrete choice models. Moreover, because CV-DCMs are grounded in random utility maximisation principles, they maintain the solid behavioural foundation of traditional discrete choice models. We demonstrate the proposed CV-DCM by applying it to data obtained through a novel stated choice experiment involving residential location choices. In this experiment, respondents faced choice tasks with trade-offs between commute time, monthly housing cost and street-level conditions, presented using images. As such, this research contributes to the growing body of literature in the travel behaviour field that seeks to integrate discrete choice modelling and machine learning

Street context of various demographic groups in their daily mobility

We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.Fil: Salgado Corrado, Ariel Olaf. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; ArgentinaFil: Li, Weixin. Massachusetts Institute of Technology; Estados UnidosFil: Alhasoun, Fahad. University of California at Berkeley; Estados UnidosFil: Caridi, Délida Inés. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Calculo. - Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Calculo; ArgentinaFil: Gonzalez, Marta. University of California at Berkeley; Estados Unido