The Analysis of Spatial Association by Use of Distance Statistics

Introduced in this paper is a family of statistics, G, that can be used as a measure of spatial association in a number of circumstances. The basic statistic is derived, its properties are identified, and its advantages explained. Several of the G statistics make it possible to evaluate the spatial association of a variable within a specified distance of a single point. A comparison is made between a general G statistic and Moran's I for similar hypothetical and empirical conditions. The empirical work includes studies of sudden infant death syndrome by county in North Carolina and dwelling unit prices in metropolitan San Diego by zip-code districts. Results indicate that G statistics should be used in conjunction with I in order to identify characteristics of patterns not revealed by the I statistic alone and, specifically, the G_i and G_i* statistics enable us to detect local “pockets” of dependence that may not show up when using global statistics.     

Left to Other Peoples’ Devices? A Political Economy Perspective on the Big Data Revolution in Development

This article brings a political economy perspective to the field of Data for Development (D4D). It highlights the fact that many projects involve extracting data from African‐based organizations for expert analysis in advanced economies. This extraction is justified on the basis that it is being used for humanitarian purposes. Key actors including the UN Global Pulse and World Economic Forum have lobbied for a governance framework emphasizing greater emission, personalization and centralization of data. The article shows how this approach enables the strategies of multinational corporations which are aiming to become data custodians of Africa's emerging economies. Little attention has been paid to the geographical distribution of capacity building nor to the ways in which data‐driven restructuring may alter existing livelihoods. As African economies become increasingly ‘digital’, data will become a source of power in economic governance. Current frameworks amount to a kind of industrial policy that supports the learning and innovation of foreign firms. The article aims to move D4D away from the focus on humanitarianism towards economic development, considering the opportunities for African citizens to benefit from their data as a source of revenue, knowledge and power. The conclusion suggests lines of inquiry for taking research further.