Applied Spatial Analysis and Policy

China’s sex ratio at birth (SRB) has drawn much attention, but contributions of individual provinces to the high national SRB have been neglected in the literature despite marked regional differences across the country. Using data from China’s censuses and inter-census surveys since 2000, we devised a decomposition method and examined the changes in provincial composition of births (PCB), parity composition by province (PCP), and male proportion by parity (MPP) and their contributions to the change in national SRB. We found that the national SRB change was most heavily influenced by the change in PCP and MPP, whereas at the provincial level the magnitude of PCB contribution was much larger than those of PCP and MPP. Due to the marked difference in SRB by parity, the decline in the proportion of first births and an increase in the proportion of higher-order births increased the national SRB. The consistent decline in the proportion of males in second and above parity births lowered national SRB, while the male proportion of first-parity births increased national SRB. Different provinces contributed to the change in national SRB at different levels of magnitude, and the above three factors exhibited regional differences.

This project sought to study the simple to state but difficult to address problem of tackling a long term entrepreneurial deficit in the North of England while adjusting to demographic change. The project was part of a programme of research into the latter but employed an array of research tools notably case studies, a telephone survey and in-depth interviews to investigate the impact of these demographic changes on new firm formation rates and the stock of businesses. Our work highlighted not only the spatial aspects of change but the opportunities created by an older population. We found growing interest in self-employment among those aged 50 and over. Men were, however, more likely to seek continuing employment, while women were more enthusiastic about self-employment and business creation. There were other differences by gender in terms of their business ambitions and types of venture they wanted to create. Attention was paid in the study to the opportunities for entrepreneurship policy interventions locally, regionally and sectorally that might emerge from the restructuring of the public sector. There are, however, barriers to capitalising on these opportunities especially the evidence of high levels of risk aversion among public sector workers.

Risk Terrain Modeling (RTM) — an innovative geospatial approach to analyze the locations of high crime areas within cities— was used to analyze criminogenic spaces and identify the riskiest places contributing to vehicle and residential burglary in the city of Coral Gables, Florida from 2004 to 2016. Official crime incident data on residential and vehicle burglary were provided by the Coral Gables Police Department. We investigated the role of environmental predictors of crime by analyzing the effects of the designated riskiest places including alcohol vendors, car dealers, gas stations, bars, secondary/post-secondary schools, grocery stores, and restaurants. We identified risky places and their proximity to the occurrence of residential and vehicle burglary with a regression process using the RTM technique to determine the Relative Risk Values (i.e., weighted risk) that each risk factor had. We examined different temporal designations, including day and night, day of the week, and monthly intervals. Our results indicated that restaurants and grocery stores located in the downtown area and along the US 1 highway were higher risk locations for the occurrence of both types of crime throughout the city. The locations of risky places in the study area were spatially consistent with high crime areas, supporting our main hypothesis. The use of RTM with targeted community policing strategies could move policy from a reactionary approach to more proactive solutions to crime prevention. Concepts drawn from routine activity theory, as well as crime and place perspectives, both receive moderate empirical support for vehicle and residential burglary outcomes.

The city of New Orleans is still recovering from Hurricane Katrina and continues to experience numerous negative effects from the lingering storm damage to its urban forest cover. The goal of this study was to analyze recent changes in area coverage of all trees, shrubs, and herbaceous lawns in neighborhoods of New Orleans, particularly in three of the most severely areas damaged by the flooding of 2005, down to the scale of individual blocks, and on all demolished lots and properties conveyed new ownership since 2010. Object-based classification for 4-band aircraft imagery from the National Agriculture Imagery Program (NAIP) was used to generate high-resolution maps of large tree canopy cover and vacant lots for the city of New Orleans. Field surveys and online map street photos were used to determine the accuracy of new city-wide maps of large tree canopy area. Based on 2017 NAIP image analysis of Katrina-flooded areas, results showed that the Lakeview neighborhood had the greatest number of large trees and the highest density of tree canopy area (at 11% cover), compared to the Lower Ninth Ward and Seventh Ward neighborhoods (both at 9% cover). However, vacant lots mapped in the Lower Ninth Ward were (on average) over twice as large in area as the vacant lot polygons in the other two neighborhoods studied. It was further estimated that 70% of the total area in the Lower Ninth Ward remains covered by vacant lots with shrub and herbaceous cover. The absolute number of lots was also highest for the Lower Ninth Ward with respect to demolished properties with a continuing net greening trend between 2010 and 2017. It is concluded that an object-based classification method can generate accurate high-resolution maps of large tree canopy cover and vacant vegetated lots for the city of New Orleans.

Since 2010, the Nordic countries have experienced substantial fertility decline. This was unexpected, as these countries have well-established systems of family support policies and they did not experience a fertility shock in the immediate aftermath of the 2008 economic recession like many other European countries. Previous studies paid little attention to the spatial dimension of this unexpected fertility change, despite evidence of large spatial variation in fertility. This paper aims to close this gap through a spatial perspective and deepen our understanding of how Nordic fertility change varies by economic and social contexts. We apply advanced spatial panel models on data for 1,099 municipalities covering Denmark, Finland, Norway, and Sweden. Next to analyzing total fertility rates, we also compare the relevance of economic and social contexts for spatial fertility variation at younger or older ages. We note distinct differences by levels of urbanization and differing trends between the age groups. We also find that measures of unemployment and union stability are associated with lower levels of fertility across space and time, while support for conservative parties is related to higher fertility. Age-specific analyses highlighted that economic conditions are more relevant for fertility variation under age thirty than over age thirty. Overall, our analysis provides support for the view that both economic and social factors are highly relevant for understanding spatial variation in the Nordic fertility decline. Given the strong spatial component in Nordic fertility change, policy initiatives would also benefit from a spatial dimension to effectively address fertility change.

Issues of malnutrition among young children in developing countries are gaining more attention of policy-makers because of the adverse effects on the well-being of people and economic of these nations. Anthropometric variables used for determining malnutrition are measured through z-scores where those whose measures fall into the extreme ends of the scores are considered malnourished. Conditional mean regression has been adopted to examine the determinants but often times, covariates would have effect on the mean, but have no substantial influence on more extreme quantiles. We adopt Bayesian quantile regression approach to measure the spatial distributions of childhood undernutrition at state and local government levels in Nigeria. Markov random fields and Bayesian P-splines were used as priors for the spatial and nonlinear components respectively and estimation was through MCMC technique. Results show the existence of north-south divide in undernutrition in Nigeria and that observed socioeconomic variables could have little influence on the distribution of undernutrition across space in the country.

To assess the implementation of Planning Support Systems (PSSs) and scenario planning strategies, a common understanding of model goals, performance, and usability are among several issues that need to be addressed. Without a common understanding, resulting policies that are drawn from model outcomes could be compromised. Reporting methods of PSS results should be objective, reasonable, understandable, and useful. We describe a process including several methods of reporting PSS results that allows for minor variation and understandably communicating the results to PSS users. This approach includes finding meaningful resolution and probability matching of PSS model results. We show that our approach is a theoretically reasonable and objective method for reporting PSS results in planning practice. We test our approach using the LEAM PSS in an application in northern Illinois. In this case, our model results inform planners using an easily comprehensible spatial resolution over which the simulation provides useful information for future land-use scenarios. We also apply our reporting methods to compare a preferred scenario to a business-as-usual scenario. The result prompts local planners and stakeholders to rethink their plan implementation strategy for preferred scenario implementation. We conclude that next steps for PSS model development will require both technical and practical focuses in the model reporting and implementation arena.

Using a combination of data derived from the U.S. Census Bureau, the Substance Abuse and Mental Health Service Administration’s Behavioral Health Systems Locator and the Boston Police Department’s Incident Reports, this study examines the spatio-temporal distribution of calls for medical assistance following a heroin-related injury between 2015 and 2018. As well, an examination is conducted regarding the accessibility of Health Care Centers (HCCs) in relation to individuals in need of medical assistance. Distance-based measures of spatial association, including nearest neighbor and K-function analysis, were used to determine the clustering of calls at various spatial scales. Structural neighborhood characteristics (i.e., distance to the closest HCC) and physical and social vulnerability were used to predict the rate of calls for a heroin-related injury per 1,000 persons in the city of Boston using a spatial autoregressive model. HCC catchment areas for the set of medical emergency calls were created using Voronoi tessellations. Results showed that the average nearest neighbor distance (NND) of each call for medical assistance was approximately 187 m or .12 miles. Calls for assistance became more probable and increasingly more spatially concentrated over the study period. The K-function analysis revealed that the calls clustered at different spatial scales and in proximity to HCCs. Average distance to the nearest HCC and Household/Disability vulnerability were significantly associated with the census tract call rate per 1,000 persons. The average travel distance between the calls for medical assistance and the nearest HCC was 941 m, or just over a half-mile. Policy implications for the provision, through HCCs, of a focused and comprehensive community-based support system in a large urban city for individuals with serious drug problems are discussed in context.