Spatial Patterns of Crime in Israel: Investigating the Effects of Inter-urban Inequality and Proximity

Many crimes in Israel, specifically property-related, are perpetrated by those who live outside localities where the crime is committed. As a result, crime rates are strongly affected by settlement patterns: Affluent localities surrounded by poor towns tend to exhibit relatively high crime rates. In order to measure the effect of urban inequality and proximity on crime rates, the Index of Relative Income (IRI) is proposed. This index is estimated as the ratio between the average income in a town and that in its neighbouring localities. As multivariate analysis indicates, the proposed index helps to explain the variation of property crime rates across urban localities, implying that the spatial unevenness of urban development (i.e. aerial proximity of affluent and poor towns) may spur property crimes. The findings of the present study lend support to regional development programs, aimed at minimizing spatial disparities in regional and urban development.

Measuring Regional Disparities in Small Countries

Though individual studies of regional disparity may deal with separate development measures - population growth, wages, welfare, regional productivity, etc. - the use of an integrated indicator is often essential, particularly if a comparative (cross-country) analysis is required. In order to measure the extent of disparities, various indices of inequality are commonly used. The goal of present study was to determine whether commonly used inequality measures (Gini, coefficient of variation, etc.) produce meaningful estimates when applied to small countries, thus making it possible to compare the results of analysis obtained for such countries with those obtained elsewhere. As we argue, a small country may differ from a country of larger size in three fundamental features. First, it is likely to have a relatively small number of regional divisions. Second, its regional divisions are likely to vary considerably in their population sizes. Lastly, regions of a small country may rapidly change their rank-order positions in the country-wide hierarchy, by changing their attributes (e.g., population and incomes). In contrast, in a large country such rank-order changes may be both less pronounced and slower-acting. In order to formalize these distinctions, we designed simple empirical tests, in which income and population distributions, presumably characteristic for small countries, were compared with a “reference” distribution, assumed to represent more accurately a country of a larger size. In the latter (reference) distribution, the population was distributed evenly across regional divisions and assumed to be static. In the first test, we checked whether the overall number of regions matters. In the second, we tested whether different inequality indices respond to differences in the regional distribution of population, viz., evenly spread population in the reference distribution vs. unevenly spread population in the test distribution. Finally, in the third test, we verified whether different inequality indices were sensitive to the sequence in which regions are introduced into the calculation. Somewhat surprisingly, none of the indices we tested appeared to pass all the tests, meaning that they may produce (at least in theory) misleading estimates if used for small countries. However, two population weighted indices – Williamson and Gini - appeared to exhibit only minor flaws and may thus be considered as more or less reliable regional inequality measures. Although further studies on the performance of different inequality indices may be needed to verify the generality of our observations, the present analysis clearly cautions against indiscriminate use of inequality indices for regional analysis and comparison.

Spatial Patterns of Urban Growth - Does Location Matter? a Case Study of Nepal

Between 1952 and 2001, the number of urban settlements in Nepal grew from 10 to 58, while their share in the country’s population increased from 2.6 to 14.4%. However, the spatial distribution of urban growth was uneven. The fastest growing urban localities are situated near major population centers, close to highways, and in the vicinity of the In-dian border. Urban localities elsewhere exhibited sluggish economic growth and poor socio-demographic performance. Data for this analysis were drawn from databases maintained by Nepal’s Central Bureau of Statistics; the Municipalities’ Association; the Ministry of Local Development and its Department of Topographical Survey. In the GIS-assisted analysis, spatial reference data (e.g., distances between individual municipalities and major rivers, roads, international borders and major population centers) were matched against five performance indexes, viz. annual population growth, per capita in-come and expenditures of local municipalities, telephone ownership, number of primary schools, and number of industries.

Development similarity based on proximity - a case study of urban clusters in Canada

The present analysis of urban clusters (UCs) in Canada deals with two matters of immediate interest: a) investigating the spatial autocorrelation of development levels in towns within such clusters, and b) ascertaining the physical sizes of UCs in Canada (i.e. the spatial extent of the area of strong inter-town development association). The present analysis leads to three general conclusions: •First, development levels of neighbouring towns in UCs of Canada tend to be closely associated, though the intensity of such a development association generally tends to decline as inter-town distances increase. As argued, this spatial association of development rates may be due to the fact that both private investors and migrants consider UCs as integrated functional units, and make their location decisions hierarchically: first, among or between town clusters, and then among or between individual towns in a 'preferred' cluster. •Second, the effect of clustering on urban growth is not uniform. It is stronger in peripheral UCs (specifically in respect to unemployment and income variables), while in centrally-located ones the development levels of neighbouring towns are less interdependent. In general, distances within which inter-town development linkages are sufficiently strong to affect or promote clustering vary with the range practicable for daily commuting, that is, from 20-40 km in the country's core and 60-100 km in its periphery. •Third, the effect of spatial proximity of towns on their functional linkages differs in respect to different development measures. In particular, as found from our analysis of Canada's core areas, only population and housing variables exhibit strong spatial associations, while the effect of spatial factors on employment-related variables – average income and unemployment rate – is weaker. This dissimilarity represents fundamental differences between these two groups of variables. That is, while population and housing variables may be confidently associated with the clustering of residents in socially homogenous areas, the spatial association of employment-related variables may be influenced by inter-urban commuting. Thus, low unemployment in a town may reflect the availability of employment in the larger region rather in the town itself, which is an important caution about the care that needs to be taken in correctly selecting and interpreting indicators of urban functionality and growth potential. An important strategic finding of the present investigation is that local towns appear to follow the path of the central city over time, and local towns adjacent to a wealthy city are likely to perform better than those around a less-prosperous central locality. This result indicates that urban growth may spread across individual towns in both core and peripheral UCs, which has implications for urban and regional development policies and programs at the municipal, provincial and federal levels of government. In particular, the findings of the present analysis thus support the creation and stimulation of UCs in areas where further urban growth is desired. According to this strategy, development resources should be concentrated on selected UCs until they become sufficiently attractive to migrants and private developers. Support of the selected localities should, of course, include a balanced investment in both the housing development and employment-generating sectors. In addition to direct government intervention, various forms of indirect involvement, such as incentives for private investors and tax exemptions can be applied. Then, and based on evidence derived from the application of impact assessment procedures, as soon as the growth of the selected UCs becomes sustainable support may be redirected to other UCs. This hierarchical concentration of resources can then be shifted into more remote areas

Spatial Patterns of Crime in Israel: Investigating the Effects of Inter-urban Inequality and Proximity

Many crimes in Israel, specifically property-related, are perpetrated by those who live outside localities where the crime is committed. As a result, crime rates are strongly affected by settlement patterns: Affluent localities surrounded by poor towns tend to exhibit relatively high crime rates. In order to measure the effect of urban inequality and proximity on crime rates, the Index of Relative Income (IRI) is proposed. This index is estimated as the ratio between the average income in a town and that in its neighbouring localities. As multivariate analysis indicates, the proposed index helps to explain the variation of property crime rates across urban localities, implying that the spatial unevenness of urban development (i.e. aerial proximity of affluent and poor towns) may spur property crimes. The findings of the present study lend support to regional development programs, aimed at minimizing spatial disparities in regional and urban development

The Change of Support Problem (COSP) and its Implications for Urban Analysis: Some Evidence from a Study of the European Urban System

The Change of Support Problem (COSP) reflects a possibility that the outcome of an urban analysis may depend critically on the researcher's choice of territorial units. To verify this assumption, the present study examines the association between population growth and population size of localities, using population growth data for two levels of geographic resolution - 4,667 local administrative units (i.e., municipalities) and 2189 contiguous urban areas in 40 European countries. According to our findings, when individual localities are considered, the growth of localities appears to be strictly proportional to population size, but 'dissipates' when the settlement system is disaggregated into two urban sub-systems, formed by well-positioned localities and poorly positioned ones. Concurrently, for urban areas, a strong positive association between population size and growth emerges both before and after controlling for location attributes. However, this association between population size and growth is not especially strong, if favorably and unfavorably located urban areas are looked at in separation

Interregional inequalities in Israel: Explanatory model and empirical data

An explanatory model of regional inequality is proposed, which attempts to explain a spatial distribution of different income groups. According to this model, such a distribution is a function of the relation between the cost of living in a particular geographic area and actual income of its inhabitants. The applicability of this model to spatial inequalities in Israel is investigated, using data from five subsequent censuses of population and housing. The analysis indicates that there is no universal trend in the development of inequalities, examined from either a temporal or a spatial point of view. Instead, the extent of interregional disparities appears to differ when various indicators of inequality are considered. Measures of population distribution and wealth indicate the highest extent of interregional disparities, whilst the country's regional development appears to be the least uneven when indicators of education and participation in the labor force are considered. Temporally, most indicators of welfare and population distribution tend to diverge over time, reflecting increasing interregional disparities. In contrast, variables related to education and housing tend to converge, indicating a reduction in inequality. Moreover, the change in inequality appears to differ across various geographic areas: Whereas development in the central part of Israel has tended to become more uniform over time, the country's peripheral regions have developed towards further polarization of their socio-economic development. As a result of the analysis, several strategies are proposed aimed at reducing the extent of interregional disparities.

Spatial Patterns of Urban Growth - Does Location Matter? a Case Study of Nepal

Between 1952 and 2001, the number of urban settlements in Nepal grew from 10 to 58, while their share in the country's population increased from 2.6 to 14.4%. However, the spatial distribution of urban growth was uneven. The fastest growing urban localities are situated near major population centers, close to highways, and in the vicinity of the In-dian border. Urban localities elsewhere exhibited sluggish economic growth and poor socio-demographic performance. Data for this analysis were drawn from databases maintained by Nepal's Central Bureau of Statistics; the Municipalities' Association; the Ministry of Local Development and its Department of Topographical Survey. In the GIS-assisted analysis, spatial reference data (e.g., distances between individual municipalities and major rivers, roads, international borders and major population centers) were matched against five performance indexes, viz. annual population growth, per capita in-come and expenditures of local municipalities, telephone ownership, number of primary schools, and number of industries

On Ecological Fallacy and Assessment Errors Stemming From Misguided Variable Selection: Investigating the Effect of Data Aggregation on the Outcome of Epidemiological Study

In behavioral studies, ecological fallacy is a wrong assumption about an individual based on aggregate data for a group. In the present study, the validity of this assumption was tested using both individual estimates of exposure to air pollution and aggregate air pollution data estimated for 1,492 schoolchildren living in the in vicinity of a major coal-fired power station in the Hadera sub-district of Israel. In 1996 and 1999, the children underwent subsequent pulmonary function (PF) tests, and their parents completed a detailed questionnaire on their health status, and housing conditions. The association between children’s PF development and their long-term exposure to air pollution was then investigated in two phases. During the first phase, the average rates of PF change observed in small statistical areas in which the children reside were compared with average levels of air pollution detected in these areas. During the second phase of the analysis, an individual pollution estimate was calculated for each child covered by the survey, using a "spatial join" tool in ArcGIS. While the analysis of aggregate data showed no significant differences in the PF development among the schoolchildren surveyed, the comparison of individual pollution estimates with the results of PF tests detected a significant negative association between changes in PF results and the estimated level of air pollution. As argued, these differences are attributed to the fact that average exposure levels are likely to cause a misclassification bias of individual exposure, as further demonstrated in the study using pattern detection techniques of spatial analysis (local Moran's I and Gettis-Ord statistic). The implications of the results of the analysis for geographical and epidemiological studies are discussed, and recommendations for public health policy are formulated.

Does Zipf's Law Hold for Primate Cities? Some Evidence from a Discriminant Analysis of World Countries

According Zipf's Law, city sizes follow a Pareto distribution, with the rank (R) of a city i being proportional to its size (S): R(i)=A*S-α or ln(R) = ln(A)-α*ln(S), where α is a slope gradient or Pareto parameter, varying around 1. However, several empirical studies, carried out to date, indicate that the sizes of the first largest cities in many countries (with ranks of 1 and 2) are not exactly given to Zip's Law, but with relatively large errors. In our study, we consider the ratio between the size of the first largest city and the size of the second largest city (B-ratio) for a very large ensemble of 177 countries across the world. A surprising result of this work is that only a small number of countries (about 35%) have their B-ratios within the limits expected under Zipf's Law (B=0.4÷0.6). As we also learn from the discriminant analysis of our country-wide data, high urbanization levels are likely to reduce the gap in population sizes between the first and the second city, while the first city being the national capital is likely to widen the gap between it and its "nearest neighbor" in the national city-size distribution