With the increasing use of geographical information and technology in a variety of knowledge domains and disciplines, the need to discover and access suitable geospatial data is imperative. Most spatial data infrastructures (SDI) provide geoportals as entry points to the SDI through which geospatial data are disseminated and shared. Geoportals are often known in geoinformation communities only, and they present technological challenges for indexing by web search engines. To overcome these challenges, we identified and categorized search terms typically employed by users when looking for geospatial resources on the Web. Guided by these terms, we published metadata about geospatial sources “directly” on the Web and performed empirical tests with search engine optimization (SEO) techniques. Two sets of HTML pages were prepared and registered with Google and Bing respectively. The metadata in one set was marked up with Dublin Core, the other with Schema.org. Analysis of the results shows that Google was more effective than Bing in retrieving the pages. Pages marked up with Schema.org were more effectively retrieved than those marked up with Dublin Core. The statistical results were significant in most of the tests performed. This research confirms that pages marked up with Schema.org and Dublin Core are a novel alternative for improving the visibility and facilitating the discovery of geospatial resources on the Web.

This paper demonstrates a simple methodological sequence for the statistical calculation of a context-sensitive quality of life index especially suitable for use in the Global South, i.e. lower and middle income countries. We draw on the large (n = 24,889), area-sampled, survey dataset of the fifth Quality of Life Survey (QoL V, 2017/18) of South Africa’s Gauteng province, conducted by the Gauteng City-Region Observatory. Using overtly exploratory analysis, and a fully reflective conception of indicators, we apply exploratory factor analysis (EFA) in two stages to generate our model, while defending our approach methodologically and empirically and indicating its philosophical foundations. We contrast this approach to previous analyses using the QoL I (2010) data, which defined their dimensions and assign their indicators in advance, drawing on literature and indexes largely from the Global North. We ran series of EFAs on 60 longitudinally available variables from the QoL V data set. This allowed us to determine the optimal number of factors/dimensions, guided by established criteria and the interpretability of the grouped indicators. Each dimension score is calculated arithmetically, using the indicators’ factor loadings as weights. Then the dimensions, weighted by their eigenvalues, are aggregated into the composite index, scaled to run from 0 to 100. The resulting seven-dimension, 33-indicator model was validated through confirmatory factor analysis (CFA) on the QoL V data; and its configural invariance, i.e. whether the pattern of indicators amongst the factors holds over time, was checked against the two previous QoL datasets and confirmed. This validates the ability of the approach outlined to generate a stable index. Analysis of the resulting quality of life index by race and municipality reveals trends consistent with the South African context. Overall, the White population group has the highest measured quality of life index, followed in turn by the Indian/Asian, Coloured and Black African population groups. Changes in the quality-of-life ranking of the nine municipalities comprising Gauteng province may be observed over time, using the validated previous models. The value of the exploratory approach in enabling context to influence the index construction is highlighted by the emergence of a distinct dimension of bottom-up political voice, which is relevant for democratic governance.

A key factor in understanding a city-region is the daily flows of people between its constituent parts. Mapping of the commuter field may help delineate the functional extent of the region, clarify challenges of transport efficiency, access and affordability, and reveal the impact of key transport interventions over time. It may also elucidate social dimensions of life in the region, notably patterns of inequality between groups. Data from the Gauteng City-Region Observatory’s (GCRO) 2013 Quality of Life Survey were used to map trips to work made by white and (separately) African respondents. The maps illustrate the functional extent of the city-region as well as ongoing racial disparities in geographic access-to-work opportunities in a region historically patterned by apartheid spatial forms. While there is significant continuity from the past, there are also some surprising new trends.

Apartheid laws resulted in racial residential segregation that became entrenched into the urban morphology of South Africa. When apar-theid ended in the 1990’s, the new South African democratic government was resolved to bring about social and spatial justice, address inequalities and promote social cohesion. To determine progress towards racial residential integration, aspatial indices of segregation are widely employed despite their shortcomings and limi-tations. This study employs two spatial indices of segregation: the spatial information theory index and the spatial exposure/isolation index in order to measure and quantify the levels of racial residential segregation that individuals living in Gauteng province (South Africa) would experience on average within radii of 500m, 4km and 8km of their respective residential locations. The analysis is based on the 1996, 2001 and 2011 Census data. The results indicate that the levels of racial residential segregation have steadily declined between 1996 and 2011. The levels of exposure of Whites to Black Africans continue to increase while the levels of exposure of Black Africans to Whites have remained unchanged over the same period. These observations are true for the different geographic scales (i.e. within radii of 500m, 4km and 8km) of analysis considered in this study.