Applied Spatial Analysis and Policy

City-driven open data initiatives have made spatially referenced crime and risk factor data more readily available online, allowing for significance tests to determine the relationship between environment and crime. This paper uses a variety of open source data to assess risk factors for specific violent crime types (assault, homicide, rape, robbery) in three different cities. The results contribute to our understanding of 1) variation in intra-city risk factors for each violent crime type, 2) the degree of spatial overlap for high-risk places for each violent crime type within a city, and 3) the generalizability of risk factors across crime types and cities. Risk Terrain Modeling (RTM) was used to determine the risk factors associated with each crime type at the micro-level and conjunctive analyses of case configurations (CACC) determined the unique behavior settings at the highest risk for each specific violent crime in each city. The findings indicate that intra-city risk factors vary greatly for each violent crime, highrisk places for different violent crimes tend to not overlap spatially within a city, and risk factors are not generalizable across crime types or across cities. Researchers and law enforcement need to examine local, crime-specific contexts when assessing crime problems and generating solutions.

Research suggests that spatial inaccessibility to food stores adversely affects the health status of individuals living in predominantly low income or racial minority geographic areas. Previous studies examining geographic accessibility to food stores have focused on defining ‘food deserts’ using a variety of methods to map and quantify inaccessibility. However, the ability to afford and purchase healthy food must also be considered, and a scan of recent research reveals few studies that have accounted for the role of government assistance programs and how this might impact people’s accessibility. In this paper, we analyze specific at-risk populations’ accessibility to Supplemental Nutrition Assistance Program (SNAP) accepting locations using GIS-based estimates of specific personal transportation costs. In the U.S., the SNAP program attempts to alleviate food insecurity among low-income groups who qualify for assistance. We focus on understanding the relative accessibility of potentially vulnerable demographic populations as captured at the Census block group level and their potential ability to reach SNAP accepting food store locations. A mid-sized city in Florida is used as a test case. Network-based approaches are employed using GIS to gauge accessibility in terms of walking, automobile, and public transit modes. Ultimately, we seek to better understand possible differences in accessibility across socioeconomic groups, emphasizing characteristics such as vehicle ownership, race, and income, while recognizing the importance of the SNAP program. Findings suggest that higher income, high vehicle access, and white populations are more accessible to food opportunities than lower income, low vehicle access, and African American populations and potential policy implications of this work include whether certain transportation costs should be subsidized for individuals receiving SNAP benefits.

We investigate how co-ethnic concentration and length of residence are related to neighborhood perception in aged Mexican Americans, and discuss sources of information for measuring social environments. Neighborhood perception scale, length of residence in current home, and all individual-level covariates in a hierarchical linear model are derived from data on community-dwelling older adults. Tract-level measures are obtained from Census data. We find no relationship between co-ethnic concentration and positive neighborhood perception, and find a direct relationship between length of residence and positive neighborhood perception. Until further evidence is found, different sources of information when measuring place should be treated equally.

Spatial flexibility of the workforce is important in clearing regional labour markets. Workers’ spatial flexibility is limited and many European countries are developing policies to enhance the spatial flexibility of the labour force. Using the 2002 Netherlands Housing Demand Survey and logistic regression models, this paper examines the determinants of both long commutes and intentions to migrate for a job. The results show that living in the highly urbanized western part of the Netherlands increases the probability that workers opt for long commutes. Living in the more peripheral regions increases the probability that workers think about moving residence. The results further show that workers with long commutes are more likely to have the intention to move residence in the near future than workers with short commutes. This indicates that long commutes are suboptimal at the individual level.

Spatial homogamy, or sharing a similarity in geographical origin, is an under-researched dimension in homogamy studies. In the Netherlands, people tend to choose spatially homogamous partners. Moreover, there is considerable regional variation in spatial homogamy, even when residential location and population density are controlled for. This study aims to explain the regional variation in spatial homogamy by means of a spatial regression. Three sets of explanations are taken into account: compositional effects, spatial determinants, and regional cultural differences. The data used consists of a unique geo-coded micro dataset on all new cohabiters in the Netherlands in 2004 (N = 289,248), combined with other data from varying sources. In the spatial regression, the dependent variable is the standardized distance coefficient, based on the distance between partners before cohabitation, standardised for the average distance to other inhabitants. We find that especially educational, income and cultural differences contribute to the regional variation in spatial homogamy.

The future of urban modelling is viewed first against a background of its fifty-decade history. The effects of increased computing power and the availability of new data sources are explored, particularly through a wider range of scales and applications – illustrated by global scales and applications as wide-ranging as defence and security and history and archaeology. The challenges of making models fully dynamic are articulated along with a recent development which introduces uncertainty into dynamic urban models through a potential function. Finally, the potential for more effective deployment of models in city planning is shown with a system that combines data assembly, modelling and interactive planning.

Timely and accurate spatial explicit forest fire risk assessment and mapping is essential for forest fire prevention and suppression preparedness, firefighting resources allocation, and efficient multi-level fire management policies. This paper describes the application and validation of an approach for forest fire risk analysis and fire risk zoning over Romania in order to identify areas at national scale where fires are most likely to occur and to threat existing values, resources and assets. A modeling approach based on logistic regression using historical fire observations has been developed based on environmental, socio-economic and demographic data availability at national scale. Ignitions were positively related to south-western slopes and occurred mostly in fuel type of xerophyte oaks as well as in areas of heterogeneous (natural/agricultural) landscape. In addition to the human variables the pattern of ignitions was also significantly related to slope and temperature of the driest quarter. The risk zones produced by the multiple logistic regression model presented satisfactory accuracy when compared with historical fire perimeters extracted from MODIS imagery. The findings of this study could be used by fire managers to implement prevention measures at forest areas with high fire risk. Furthermore, attention should be given to areas with high fire ignition probability, where the vulnerability and potential impact is higher.