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The multivariate pluriformity and complexity of economic-geographic space (e.g., cities or countries) are reflected in their empirical multidimensional data structure with space–time characteristics. The need to reduce the multiple dimensions of an observation space is present in all social (and other) sciences seeking to identify basic patterns or key relations among critical indicators that characterize economic or social features of the phenomena concerned. For this purpose, multivariate statistics has developed an impressive toolbox, in which traditionally a prominent place is taken by the class of principal component analyses (PCA). This technique dates back to the beginning of the last century and is widely employed in empirical research aiming at reducing complexity in observation spaces toward manageable patterns of a smaller dimensionality. In the present study, we develop and present a new methodological contribution to in the PCA field, by shifting from conventional discrete static data to time-series data approximated by a continuous intertemporal curve reflecting the evolution of the socioeconomic data concerned. In this paper, the statistical foundation of this new approach, called multivariate functional principal component analysis (MFPCA), will be outlined and tested for a multivariate long-range data set on statistical indicators for several countries. The practical validity of the MFPCA method will be demonstrated by means of an application to the evolution of socioeconomic competitiveness (in this paper, we use the WEF definition of competitiveness, which is: "Competitiveness is the set of institutions, policies, and factors that determine the level of productivity of a country" WEF 2015) in different countries of the world, based on official World Economic Forum (WEF) data spanning the period 2008–2015. Our analysis brings to light interesting findings and differences compared to the initial, officially published WEF information. [ABSTRACT FROM AUTHOR]

The present study aims to demonstrate the importance of digital data for investigating space-time dynamics of aggregated human activity in urban systems. Such dynamics can be monitored and modelled using data from mobile phone operators regarding mobile telephone usage. Using such an extensive dataset from the city of Amsterdam, this paper introduces space-time explanatory models of aggregated human activity patterns. Various modelling experiments and results are presented, which demonstrate that mobile telephone data are a good proxy of the space-time dynamics of aggregated human activity in the city. [ABSTRACT FROM AUTHOR]

The current literature often values intangible goods like cultural heritage by applying stated preference methods. In recent years, however, the increasing availability of large databases on real estate transactions and listed prices has opened up new research possibilities and has reduced various existing barriers to applications of conventional (spatial) hedonic analysis to the real estate market. The present paper provides one of the first applications using a spatial autoregressive model to investigate the impact of cultural heritage-in particular, listed buildings and historic-cultural sites (or historic landmarks)-on the value of real estate in cities. In addition, this paper suggests a novel way of specifying the spatial weight matrix-only prices of sold houses influence current price-in identifying the spatial dependency effects between sold properties. The empirical application in the present study concerns the Dutch urban area of Zaanstad, a historic area for which over a long period of more than 20 years detailed information on individual dwellings, and their market prices are available in a GIS context. In this paper, the effect of cultural heritage is analysed in three complementary ways. First, we measure the effect of a listed building on its market price in the relevant area concerned. Secondly, we investigate the value that listed heritage has on nearby property. And finally, we estimate the effect of historic-cultural sites on real estate prices. We find that, to purchase a listed building, buyers are willing to pay an additional 26.9 %, while surrounding houses are worth an extra 0.28 % for each additional listed building within a 50-m radius. Houses sold within a conservation area appear to gain a premium of 26.4 % which confirms the existence of a 'historic ensemble' effect. [ABSTRACT FROM AUTHOR]

In this paper, we present a review of various computational experiments concerning neural network (NN) models developed for regional employment forecasting. NNs are nowadays widely used in several fields because of their flexible specification structure. A series of NN experiments is presented in the paper, using two data sets on German NUTS-3 districts. Individual forecasts are computed by our models for each district in order to answer the following question: How relevant are NN parameters in comparison to NN structure? Comprehensive testing of these parameters is limited in the literature. Building on different specifications of NN models-in terms of explanatory variables and NN structures-we propose a systematic choice of NN learning parameters and internal functions by means of a sensitivity analysis. Our results show that different combinations of NN parameters provide significantly varying statistical performance and forecasting power. Finally, we note that the sets of parameters chosen for a given model specification cannot be light-heartedly applied to different or more complex models. [ABSTRACT FROM AUTHOR]

The Spatial Interaction Model proposed by Alonso as “Theory of Movements” offers an innovative specification of spatial origin-destination flow models. Equations for flows between regions, total outflow from and total inflow to a region are linked by balancing factors. This paper presents a consistent formulation of Spatial Interaction Models in the Wilson tradition and Alonso's Theory of Movements. The paper is intended as an introduction to the model and a review of␣the state of the art. Besides it is argued that simultaneous equation techniques are required to estimate the so-called systemic parameters. [ABSTRACT FROM AUTHOR]