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Using light-at-night (LAN) satellite data for identifying clusters of economic activities in Europe

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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 \((\hbox {R}^{2}=0.718{-}0.934)\). 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.

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Notes

  1. Although different types of economic activity indices were analyzed, in the following discussion we report only the models for the density of the employed in specific types of economic activity (EDx), which appeared to be best performing in terms of generality and explanatory power.

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Correspondence to Boris A. Portnov.

Appendices

Appendix A: Descriptive statistics of the research variables

See Table 7.

Table 7 The descriptive characteristics of the variables used in the analysis (year-2000)

Appendix B: Economic activity indices used in the study

In the study we used four groups of indices for economic activities, classified according to NACE classification (EM 2014):

  • density of the employed in economic activity \(x\) in region \(i\)\(ED_{xi}\) (persons per \(\hbox {km}^{2}\));

  • gross value added by economic activity per employed in economic activity \(x\) in region \(i\)\(GVA_{xi}\) (€ per person);

  • employment location quotient of economic activity \(x\) in region \(i\)

    $$\begin{aligned} \textit{LQ}(ED_{xi} )=\frac{{\frac{\textit{ED}_{xi} }{\sum \limits _x {\textit{ED}_{xi} } }}}{{\frac{\sum \limits _i {\textit{ED}_{xi} } }{\sum \limits _x {\sum \limits _i {\textit{ED}_{xi} } } }}} \end{aligned}$$
  • gross value added location quotient of economic activity \(x\) in region \(i\)

    $$\begin{aligned} \textit{LQ}(\textit{GVA}_{xi} )=\frac{{\frac{\textit{GVA}_{xi} }{\sum \limits _x {\textit{GVA}_{xi} } }}}{{\frac{\sum \limits _i {\textit{GVA}_{xi} } }{\sum \limits _x {\sum \limits _i {\textit{GVA}_{xi} } } }}} \end{aligned}$$

Appendix C: Analysis of the input data for randomness and normality and applied transformation procedures

Normality assumption is an essential precondition for multivariate regression analysis (IBM SPSS 214). To confirm that the distribution of the dependent variable is normal, the Kolmogorov–Smirnov/Lillifors test was run and its results, separately for each dependent variable used in the analysis, are reported in the Table below. The analysis was performed in the in StatPlus 2009 v.5.8.4 software.

When deviations from normality were detected, the original values of the variables were transformed using the Box-Cox procedure. The results of normality retesting of the transformed variables are also reported in the table below (see the bottom part). The data transformations were performed using the Past \(3.01^{\mathrm{TM}}\) software.

According to the Box-Cox transformation procedure, optimal transformation quotients were determined to equal: \(8.148E\)–01—for EDgi, \(1.641E{-}01\)—for \(\hbox {ED}j\), \(-4.396E{-}02\)—for \(\hbox {ED}k\), \(-7.271E{-}02\)—for EDmn, \(5.186E{-}01\)—for EDoq, and \(1.392E{-}01\)—for EDru. Models 1–6 in Table 1 predict transformed values of these variables (i. e. \(\hbox {ED}gi{^\circ }, \hbox { ED}j{^\circ },\hbox { ED}k{^\circ },\hbox { ED}mn{^\circ },\hbox { ED}oq{^\circ }\) and \(\hbox {ED}ru{^\circ }\)), using these transformation quotients.

In order to obtain regular predicted values measured on the original scales of the variables, the inverse transformations were applied (Tables 8, 9).

Table 8 Normality test of the dependent variables before and after Box-Cox transformation (variables before transformation)
Table 9 Normality test of the dependent variables before and after Box-Cox transformation (variables after transformation)

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Rybnikova, N.A., Portnov, B.A. Using light-at-night (LAN) satellite data for identifying clusters of economic activities in Europe. Lett Spat Resour Sci 8, 307–334 (2015). https://doi.org/10.1007/s12076-015-0143-5

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