Abstract: Enterprises organized in clusters are often efficient in stimulating urban development, productivity and profit outflows. Identifying the clusters of economic activities thus becomes an important step in devising regional development policies, aimed at the formation of clusters of economic activities in geographic areas in which this objective is desirable. However, a major problem with the identification of such clusters stems from limited reporting by individual countries and administrative entities on the regional distribution of specific economic activities, especially for small regional subdivisions. In this study, we test a possibility that missing data on geographic concentrations of economic activities in the European NUTS3 regions can be reconstructed using light-at-night satellite measurements, and that such reconstructed data can then be used for cluster identification. The matter is that light-at-night, captured by satellite sensors, is characterized by different intensity, depending on its source—production facilities, services, etc. As a result, light-at-night can become a marker of different types of economic activities, a hypothesis that the present study confirms. In particular, as the present analysis indicates, average light-at-night intensities emitted from NUTS3 regions help to explain up to 94 % variance in the areal density of several types of economic activities, performing especially well for professional, scientific and technical services (R^2=0.742−0.939), public administration (R^2=0.642−0.934), as well as for arts, entertainment and recreation (R^2=0.718−0.934). As a result, clusters of these economic activities can be identified using light-at-night data, thus helping to supplement missing information and assist regional analysis.

Abstract: The defense meteorological satellite program (DMSP) operational linescan system (OLS) sensors have imaged emitted light from Earth's surface since the 1970s. Temporal overlap in the missions of 5 OLS sensors allows for intercalibration of the annual composites over the past 19 years (Elvidge et al., 2009). The resulting image time series captures a spatiotemporal signature of the growth and evolution of lighted human settlements and development. We use empirical orthogonal function (EOF) analysis and the temporal feature space to characterize and quantify patterns of temporal change in stable night light brightness and spatial extent since 1992. Temporal EOF analysis provides a statistical basis for representing spatially abundant temporal patterns in the image time series as uncorrelated vectors of brightness as a function of time from 1992 to 2009. The variance partition of the eigenvalue spectrum combined with temporal structure of the EOFs and spatial structure of the PCs provides a basis for distinguishing between deterministic multi-year trends and stochastic year-to-year variance. The low order EOFs and principal components (PC) space together discriminate both earlier (1990s) and later (2000s) increases and decreases in brightness. Inverse transformation of these low order dimensions reduces stochastic variance sufficiently so that tri-temporal composites depict potentially deterministic decadal trends. The most pronounced changes occur in Asia. At critical brightness threshold we find an 18% increase in the number of spatially distinct lights and an 80% increase in lighted area in southern and eastern Asia between 1992 and 2009. During this time both China and India experienced a &#8764;20% increase in number of lights and a &#8764;270% increase in lighted area – although the timing of the increase is later in China than in India. Throughout Asia a variety of different patterns of brightness increase are apparent in tri-temporal brightness composites – as well as some conspicuous areas of apparently decreasing background luminance and, in many places, intermittent light suggesting development of infrastructure rather than persistently lighted development. Vicarious validation using higher resolution Landsat imagery verifies multiple phases of urban growth in several cities as well as the consistent presence of low DN (<&#8764;15) background luminance for many agricultural areas. Lights also allow us to quantify changes in the size distribution and connectedness of different intensities of development. Over a wide range of brightnesses, the size distributions of spatially contiguous lighted area are consistent with power laws with exponents near &#8722;1 as predicted by Zipf's Law for cities. However, the larger lighted segments are much larger than individual cities; they correspond to vast spatial networks of contiguous development (Small et al., 2011).