Search

An enumeration of spatial autocorrelation’s (SA’s) polyvalent forms occurred nearly three decades ago. Attempts to conceive and disseminate a clearer explanation of it employ metaphors seeking to better relate SA to a student’s or spatial scientist’s personal knowledge databank. However, not one of these uses the jigsaw puzzle metaphor appearing in this paper, which exploits an analogy between concrete visual content organization and abstract map patterns of attributes. It not only makes SA easier to understand, which furnishes a useful pedagogic tool for teaching novices and others about it, but also discloses that many georeferenced data should contain a positive–negative SA mixture. Empirical examples corroborate this mixture’s existence, as well as the tendency for marked positive SA to characterize remotely sensed and moderate (net) positive SA to characterize socio-economic/demographic, georeferenced data.

An enumeration of spatial autocorrelation's (SA's) polyvalent forms occurred nearly three decades ago. Attempts to conceive and disseminate a clearer explanation of it employ metaphors seeking to better relate SA to a student's or spatial scientist's personal knowledge databank. However, not one of these uses the jigsaw puzzle metaphor appearing in this paper, which exploits an analogy between concrete visual content organization and abstract map patterns of attributes. It not only makes SA easier to understand, which furnishes a useful pedagogic tool for teaching novices and others about it, but also discloses that many georeferenced data should contain a positive–negative SA mixture. Empirical examples corroborate this mixture's existence, as well as the tendency for marked positive SA to characterize remotely sensed and moderate (net) positive SA to characterize socio-economic/demographic, georeferenced data. [ABSTRACT FROM AUTHOR]

Applying the panel data of 16 districts in Beijing, China from 2009 to 2020 as the research object, this study measures and analyzes the carbon emission intensity and the level of industrial structure upgrading. Based on the above results, a spatial econometric model is established to analyze the spatial spillover effect of industrial structure upgrading on carbon emission intensity. Conclusions are drawn as follows: (a) In 2009, 2015 and 2020, the carbon emission intensity in most districts of Beijing has decreased, and in some areas even decreased significantly. The upgrading of industrial structure in all districts has been improved (b). According to the results of spatial autocorrelation, the carbon emission intensity in Beijing shows significant positive spatial autocorrelation in 2009 and 2020, while negative spatial autocorrelation in 2015; The upgrading of industrial structure in Beijing shows significant positive spatial autocorrelation in 2009, 2015 and 2020 (c). The regression results of the spatial econometric model show that industrial structure upgrading not only reduces the carbon emission intensity of the region, but also decreases the carbon emission intensity of the surrounding areas.

The article presents a study which demonstrates the multispecies and multi-scale spatial patterns of pelagic longline fisheries in the Atlantic and Pacific Oceans. The study utilizes a range of analytical approaches in order to determine the spatial patterns in bycatch data. The study revealed a clear evidence of spatial clustering found in the bycatch species in both ocean basins. It also identified a significant spatial overlap in the bycatch pattern across species which is related to the spatial distribution in fishing effort and target catch. The results of study highlights the importance of considering the relationship of bycatch and target catch.

The benefits of having a Bike Sharing System (BSS) in a city are numerous. Among other advantages, it promotes a cleaner environment with less traffic and pollution. One major problem the users of such services encounter is that of full or empty stations, causing user dissatisfaction. The objective of this work is to propose a new user-based incentive method to enhance BSS performance. The proposed method relies on a spatial outlier detection algorithm. It consists of adapting the departure and arrival stations of the users to the BSS state by stimulating the users to change their journeys in view of minimizing the number of full and empty stations. Experiments are carried out to compare our proposed method to some existing methods for enhancing the resource availability of BSSs, and they are performed on a real dataset issued from a well-known BSS called Velib. The results show that the proposed strategy improves the availability of BSS resources, even when the collaboration of users is partial. [ABSTRACT FROM AUTHOR]