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Diversity within a population has been linked to levels of both social cohesion and crime. Neighborhood crimes are the result of a complex set of factors, one of which is weak community cohesion. This article seeks to explore the impacts of diversity on burglary crime in a range of neighborhoods, using Leeds, UK as a case study. We propose a new approach to quantifying the correlates of burglary in urban areas through the use of diversity metrics. This approach is useful in unveiling the relationship between burglary and diversity in urban communities. Specifically, we employ stepwise multiple regression models to quantify the relationships between a number of neighborhood diversity variables and burglary crime rates. The results of the analyses show that the variables that represent diversity were more significant when regressed against burglary crime rates than standard socio‐demographic data traditionally used in crime studies, which do not generally use diversity variables. The findings of this study highlight the importance of neighborhood cohesion in the crime system, and the key place for diversity statistics in quantifying the relationships between neighborhood diversities and burglary. The study highlights the importance of policy planning aimed at encouraging community building in promoting neighborhood safety.

This paper analyzes rainfall variability in Argungu area. Data for half climatic year 1995-2012 were obtained from Argungu station of Kebbi Agriculture and Rural Development Authority. Analysis of variance (ANOVA) was used to analyze the data. The results show that there is statistically significant difference in annual rainfall over the years. Analysis further revealed that the month of August, 2010 has the highest rainfall amount of 1066mm with the year 1996 receiving the least annual rainfall amount within the period under study respectively. The study recommends that since annual rainfall in the area is characterized by fluctuations, irrigation agriculture should be developed and supported by government to compliment rain-fed agriculture to encourage crop production in the area.

This study uses socioeconomic and demographic data to demonstrate the value of a novel multidimensional approach to healthcare accessibility. The optimum location for healthcare facilities in relation to demand areas was determined using location-allocation models and local multiscale geographically weighted regression (MGWR) to explore spatially non-stationary relationships. The result shows that the potential accessibility of a community to primary healthcare depends on the geographic and socioeconomic characteristics of various places. The results of this study may be used to inform policy planning and decision-making for increasing accessibility to healthcare services, particularly in rural areas for achieving the Sustainable Development Goals (SDGs).

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