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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]

Urban density is central to urban research and planning and can be defined in numerous ways. Most measures of urban density however are biased by arbitrary chosen spatial units at their denominator and ignore the relative location of elementary urban objects within those units. We solve these two problems by proposing a new graph-based density index which we apply to the case of buildings in Belgium. The method includes two main steps. First, a graph-based spatial descending hierarchical clustering (SDHC) delineates clusters of buildings with homogeneous inter-building distances. A Moran scatterplot and a maximum Cook's distance are used to prune the minimum spanning tree at each iteration of the SDHC. Second, within each cluster, the ratio of the number of buildings to the sum of inter-building distances is calculated. This density of buildings is thus defined independently of the definition of any basic spatial unit and preserves the built-up topology, i.e. the relative position of buildings. The method is parsimonious in parameters and can easily be transferred to other punctual objects or extended to account for additional attributes. [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.

Typical geotechnical testing results reflect the level of soil uncertainty, which requires statistical corrections of the data for an appropriate engineering decision. This study proposes frameworks to detect outlying data points using statistical analyses, the cross-validation-based method and the generalised extreme value distribution-based method. The borehole data regarding soil depth distribution in a central area of Seoul, South Korea are assessed to validate the aforementioned methods for comparison with the distribution-based method and the Moran scatterplot method. The results show that the proposed methods enable more reliable spatial distributions to be achieved with a quantitative evaluation of local reliability. [ABSTRACT FROM AUTHOR]

A significant Geographic Information Science (GIS) issue is closely related to spatial autocorrelation, a burning question in the phase of information extraction from the statistical analysis of georeferenced data. At present, spatial autocorrelation presents two types of measures: continuous and discrete. Is it possible to use Moran's I and the Moran scatterplot with continuous data? Is it possible to use the same methodology with discrete data? A particular and cumbersome problem is the choice of the spatial-neighborhood matrix (W) for points data. This paper addresses these issues by introducing the concept of covariogram contiguity, where each weight is based on the variogram model for that particular dataset: (1) the variogram, whose range equals the distance with the highest Moran I value, defines the weights for points separated by less than the estimated range and (2) weights equal zero for points widely separated from the variogram range considered. After the W matrix is computed, the Moran location scatterplot is created in an iterative process. In accordance with various lag distances, Moran's I is presented as a good search factor for the optimal neighborhood area. Uncertainty/transition regions are also emphasized. At the same time, a new Exploratory Spatial Data Analysis (ESDA) tool is developed, the Moran variance scatterplot, since the conventional Moran scatterplot is not sensitive to neighbor variance. This computer-mapping framework allows the study of spatial patterns, outliers, changeover areas, and trends in an ESDA process. All these tools were implemented in a free web e-Learning program for quantitative geographers called SAKWeb© (or, in the near future, myGeooffice.org). [ABSTRACT FROM AUTHOR]

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]