River flooding has become a widely distributed and devastating natural disaster that has caused significant damages both economically and socially. Recently, it displaced millions of people in Nigeria and submerged several square kilometres of landed area in general and farmlands in particular. Although, the National Emergency Management Agency (NEMA) predicted the occurrence of the flood disaster and advised the relocation of residence from the floodplain to the high ground, but spatial information pertaining to the areal extent vulnerable to the hazard was not made available. This study attempted to assess the spatial impact of the October 2012 flooding of the Niger-Benue basin on the surrounding areas using the moderate resolution imaging Spectroradiometre (MODIS) data of NASA Terra satellite and developed a geospatial methodology for detecting and extracting the flood risk areas and the vulnerable population to flooding within the basin. The integration of remotely sensed data and other spatial and non-spatial data within the GIS platform was able to produce series of thematic maps which was used to generate a geospatial database for flood risk analysis and assessment. The result of the analysis effectively demonstrated the contribution of geospatial methods in mitigating and monitoring the effect of flooding along the Niger-Benue basin. It was therefore, suggested that government agencies and policy makers should adopt this powerful technique for reliable and well synthesized information which is a vital component of flood risk assessment and planning.

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The paper investigates the emerging pattern of journey to work traffic that characterises the employment centres of a fast growing African city with reference to the case of Benin region, Nigeria. This is achieved by identifying and extracting the significant employment centres of the region. On the one hand, factor analysis and Getis-Ord statistic were systematically used to identify the spatial configuration of the region’s employment. Regression models on the other hand, were used to estimate the relationship that exists between job decentralisation and travel behaviour. Factor analysis and Getis-Ord statistic identified four significant employment clusters in the region. Multivariate and bivariate regression models were further used to explore the dynamics of commuting behaviour in response to decentralisation of employment centres. It is found that employment spatial structure exerts significant influence on all dimensions of commuting pattern of the region. The result shows that decentralisation of jobs in the metropolis has led to a reduction in commuting times, travel distance and significantly influence the modal choice of commuters.

 Indonesia lied among the three of world major plates so that several districts along the southern coast of Java Island were vulnerabled to the tsunami including Lumajang. South coast of Lumajang had high population density and settlements and high levels of government and economic activity. Therefore, it is necessary to know the level of insecurity and vulnerability to the tsunami in order to be utilized as input of mitigation data for the preparation of regional spatial plans (RTRW) based on tsunami risk level. The objective of this research is to arrange the regional risk map for tsunami in Lumajang Regency using Geographic Information System (GIS) through approach of insecurity and vulnerability analysis of tsunami. The insecurity rate is analyzed based on seismicity map and run-up data of tsunami event in Lumajang District. Vulnerability approach used multicriteria such as land elevation, slope, coastal morphometry, land use, distance from the coast and distance from the river. The methodology that was used included data collections of both primary and secondary data such as satellite imagery of earth map, Lumajang statistical data. Each vulnerability data variable was processed to result a weighting and scores that its become the parameters for making a regional tsunami vulnerability map. The results showed three level of risks in five subdistricts that directly adjacent to the Southern Coast such as Yosowilangun, Kunir, Tempeh, Pasirian, and Tempursari. The high tsunami risk which covered almost along the coast, the ramps morphology, without any protective vegetation and human activities at the site while the medium of tsunami risk which were in areas with elevation more higher than the coastal and the low of tsunami risk had variations of topography, quite far from the coast and less human activities.

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Global statistical techniques often assume homogeneity of relationships between dependent variable and predictors across space. This assumption has been criticized by statistical geographers as a fundamental weakness that may yield misleading result when it is applied to dataset with spatial context. To strengthen this weakness, a new method that accounts for heterogeneity in relationships across geographic space has been presented. This is one of the family of local spatial statistical techniques referred to as geographically weighted regression (GWR). The method captures non-stationarity of relationship in spatial data that the ordinary least square (OLS) regression fails to account for. Thus, the paper is designed to explore and analyze the spatial relationships between cholera occurrence and household sources of water supply using GIS-based GWR, also to compare the modeling fitness of OLS and GWR. Vector dataset (spatial) of the study region by state levels and statistical data (non-spatial) on cholera cases, household sources of water supply and population data were used in this exploratory analysis. The result shows that GWR is a significant improvement on the global model. Comparing both models with the AICc value and the R 2 value revealed that for the former, the value is reduced from 698.7 (for OLS model) to 691.5 (for GWR model). For the latter, OLS explained 66.4 percent while GWR explained 86.7 percent. This implies that local model's fitness is higher than global model. In addition, the empirical analysis revealed that cholera occurrence in the study region is significantly associated with household sources of water supply. This relationship, as detected by GWR, largely varies across the region.

This study explored the residential choice of households based on the discrete choice model. Although, many authors in Africa cities have commented on this choice behavioural process. Notwithstanding, empirical study in this area is still lacking. There is a need to provide understanding on the household choice and decisions that influences the structure of urban landscape. A sector-wise model was formulated and used to estimate the choice behaviour of 1100 households in the area. However, the model shows that there exists somewhat complex household sorting pattern which is initiated by sociocultural, socioeconomic, accessibility and neighbourhood composition. Among these, sociocultural factors show robust influence. Such factors support that African households tend to develop strong ties to their origin. Generally, the model shows that the pattern of household choices in the region is distinct across sectors.