Deferred decentralized movement pattern mining for geosensor networks

This article presents an algorithm for decentralized (in-network) data mining of the movement pattern flock among mobile geosensor nodes. The algorithm DDIG (Deferred Decentralized Information Grazing) allows roaming sensor nodes to ‘graze’ over time more information than they could access through their spatially limited perception range alone. The algorithm requires an intrinsic temporal deferral for pattern mining, as sensor nodes must be enabled to collect, memorize, exchange, and integrate their own and their neighbors' most current movement history before reasoning about patterns. A first set of experiments with trajectories of simulated agents showed that the algorithm accuracy increases with growing deferral. A second set of experiments with trajectories of actual tracked livestock reveals some of the shortcomings of the conceptual flocking model underlying DDIG in the context of a smart farming application. Finally, the experiments underline the general conclusion that decentralization in spatial computing can result in imperfect, yet useful knowledge.     

Ad hoc shared‐ride trip planning by mobile geosensor networks

Recent developments in miniaturization of computing devices, in location‐sensing technology and in ubiquitous short‐range wireless networks enable new types of social behaviour. This paper investigates one novel application of these technologies, ad hoc inner‐urban shared‐ride trip planning: Transportation clients such as pedestrians are seeking ad hoc shared rides from transportation hosts such as private automobiles, buses, taxi cabs or trains. While centralized trip planners are challenged by assigning clients and hosts in an ad hoc manner, in particular for non‐scheduled hosts, we consider the transportation network as a mobile geosensor network of agents that interact locally by short‐range communication and heuristic wayfinding strategies. This approach is not only fully scalable; we can also demonstrate that with short‐range communication, and hence, incomplete transportation network knowledge a system still can deliver near‐to‐optimal trips.     

Using geolifespans to model dynamic geographic domains

Geographic objects are characterized by having different durations of existence, or geolifespans. A typology based on the concept of a geolifespan is developed to model variations in the longevity of entities that are stored in geographic information systems. The typology consists of two upper-level classes: Persistent and Ephemeral. The Ephemeral class is composed of three subclasses: Temporary, Transient and Brief. The set of possible transitions between classes in the typology is described, capturing how objects can change from one class to another, e.g., from Temporary to Brief or from Transient to Persistent. A transition sequence models the geolifespan class(es) to which an object belongs over a period of time and captures the evolution of dynamic geographic objects with respect to their longevity. Geolifespan classes are applied to scenarios of spatial change as well as a geosensor network to illustrate their role in modelling geographic dynamics.     

An efficient incremental algorithm for generating the characteristic shape of a dynamic set of points in the plane

Several algorithms have been proposed to generate a polygonal ‘footprint’ to characterize the shape of a set of points in the plane. One widely used type of footprint is the χ-shape. Based on the Delaunay triangulation (DT), χ-shapes guaranteed to be simple (Jordan) polygons. This paper presents for the first time an incremental χ-shape algorithm, capable of processing point data streams. Our incremental χ-shape algorithm allows both insertion and deletion operations, and can handle streaming individual points and multiple point sets. The experimental results demonstrated that the incremental algorithm is significantly more efficient than the existing, batch χ-shape algorithm for processing a wide variety of point data streams.     

Finding community structure in spatially constrained complex networks

One feature discovered in the study of complex networks is community structure, in which vertices are gathered into several groups where more edges exist within groups than between groups. Many approaches have been developed for identifying communities; these approaches essentially segment networks based on topological structure or the attribute similarity of vertices, while few approaches consider the spatial character of the networks. Many complex networks are spatially constrained such that the vertices and edges are embedded in space. In geographical space, nearer objects are more related than distant objects. Thus, the relations among vertices are defined not only by the links connecting them but also by the distance between them. In this article, we propose a geo-distance-based method of detecting communities in spatially constrained networks to identify communities that are both highly topologically connected and spatially clustered. The algorithm is based on the fast modularity maximisation (CNM) algorithm. First, we modify the modularity to geo-modularity Q^geo by introducing an edge weight that is the inverse of the geographic distance to the power of n. Then, we propose the concept of a spatial clustering coefficient as a measure of clustering of the network to determine the power value n of the distance. The algorithm is tested with China air transport network and BrightKite social network data-sets. The segmentation of the China air transport network is similar to the seven economic regions of China. The segmentation of the BrightKite social network shows the regionality of social groups and identifies the dynamic social groups that reflect users’ location changes. The algorithm is useful in exploring the interaction and clustering properties of geographical phenomena and providing timely location-based services for a group of people.     

Leveraging correlation across space and time to interpolate geophysical data via CoKriging

Managing geophysical data generated by emerging spatiotemporal data sources (e.g. geosensor networks) presents a growing challenge to Geographic Information System science. The presence of correlation poses difficulties with respect to traditional spatial data analysis. This paper describes a novel spatiotemporal analytical scheme that allows us to yield a characterization of correlation in geophysical data along the spatial and temporal dimensions. We resort to a multivariate statistical model, namely CoKriging, in order to derive accurate spatiotemporal interpolation models. These predict unknown data by utilizing not only their own geosensor values at the same time, but also information from near past data. We use a window-based computation methodology that leverages the power of temporal correlation in a spatial modeling phase. This is done by also fitting the computed interpolation model to data which may change over time. In an assessment, using various geophysical data sets, we show that the presented algorithm is often able to deal with both spatial and temporal correlations. This helps to gain accuracy during the interpolation phase, compared to spatial and spatiotemporal competitors. Specifically, we evaluate the efficacy of the interpolation phase by using established machine-learning metrics (i.e. root mean squared error, Akaike information criterion and computation time).     

Optimization of constitutive parameters of foundation soils k-means clustering analysis

The goal of this study was to optimize the constitutive parameters of foundation soils using a k-means algorithm with clustering analysis. A database was collected from unconfined compression tests, Pr...     

Influence of Local Frequent Dynamic Disturbance on Micro-structure Evolution of Coal-Rock and Localization Effect

This study aimed to investigate the influence of local frequent dynamic disturbance on micro-structure evolution in different zones of coal-rock. To do so, we carried out a systematic experimental research on the micro-structure evolution of briquette and raw coal samples under local impact load by using self-developed pendulum hammer dynamic impact loading test device of coal-rock and ultrasonic testing equipment, and analyzed the localization effect of local impact load. The results show that M_n (micro-structure cumulative change factor) of briquette coal samples presents an inclined M-shaped four-stage evolution mode along and perpendicular to impact direction with cyclic impact times under conventional full impact load, whereas it shows more obvious anisotropy and localization under local impact load. M_n for both conventional full impact and local impact shows a nonlinear increasing trend with the increase in impulse, but their increasing gradients are different. The critical zone is the most affected, the impact zone comes next, and the non-impact zone is the least affected with the increase in impulse under local impact load. M_n in the impact zone and critical zone decreases exponentially with the increase in the impact loading area, while it increases exponentially in the non-impact zone. The micro-structures evolution in briquette and raw coal samples is similar, but the anisotropy and localization effect of micro-structure evolution for raw coal samples are more significant and more sensitive to the impact loading area. The micro-structure evolution of coal-rock under local impact load shows obvious localization effect. M_n in the critical zone is usually the largest, M_n in the impact zone is slightly less than that in the critical zone, and M_n in the non-impact zone is the least. The larger the impact loading area, the wider the influence enhancement area, and the smaller the non-influence area, yet the smaller the impact zone and critical zone are affected by local impact load.

     

The Role of Community Identity in Cattlemen Response to Florida Panther Recovery Efforts

The concept of community identity has often been employed to explain ways in which communities respond to agents that impact community well-being. Using a case study of the Florida cattlemen community, we examine how cattlemen understand and perceive regulatory efforts to recover the Florida panther on private ranch lands. The data comes from participants in the Florida cattlemen community, and was collected through in-depth interviews (n = 13), group interviews (n = 32), and written comments associated with a survey about panther conservation (n = 78). Our findings indicate that some cattlemen in Florida have a strong sense of community identity. Perceptions of government actions and variation in economic risks are critical factors in understanding how this community responds to federal interventions. Our findings suggest that the concept of community identity can be used to explain the responses of agricultural landowners to governmental policies that are perceived as a threat to collective identity.     

Modeling uncertainty of moving objects on road networks via space–time prisms

Moving objects produce trajectories, which are typically observed in a finite sample of time‐stamped locations. Between sample points, we are uncertain about the moving objects's location. When we assume extra information about an object, for instance, a (possibly location‐dependent) speed limit, we can use space–time prisms to model the uncertainty of an object's location.

Until now, space–time prisms have been studied for unconstrained movement in the 2D plane. In this paper, we study space–time prisms for objects that are constrained to travel on a road network. Movement on a road network can be viewed as essentially one‐dimensional. We describe the geometry of a space–time prism on a road network and give an algorithm to compute and visualize space–time prisms. For experiments and illustration, we have implemented this algorithm in MATHEMATICA.

Furthermore, we study the alibi query, which asks whether two moving objects could have possibly met or not. This comes down to deciding if the chains of space–time prisms produced by these moving objects intersect. We give an efficient algorithm to answer the alibi query for moving objects on a road network. This algorithm also determines where and when two moving objects may have met.     

TMPA降水数据在澜沧江流域干旱监测中的评估(英文)

Drought is one of the most destructive disasters in the Lancang River Basin, which is an ungauged basin with strong heterogeneity on terrain and climate. Our validation suggested the version-6 monthly TRMM multi-satellite precipitation analysis (TMPA; 3B43 V.6) product during the period 1998 to 2009 is an alternative precipitation data source with good accuracy. By using the standard precipitation index (SPI), at the grid point (0.25°×0.25°) and sub-basin spatial scales, this work assessed the effectiveness of TMPA in drought monitoring during the period 1998 to 2009 at the 1-month scale and 3-months scale; validated the monitoring accuracy of TMPA for two severe droughts happened in 2006 and 2009, respectively. Some conclusions are drawn as follows. (1) At the grid point spatial scale, in comparison with the monitoring results between rain gauges (SPI1g) and TMPA grid (SPI1s), both agreed well at the 1-month scale for most of the grid points and those grid points with the lowest critical success index (CSI) are distributed in the middle stream of the Lancang River Basin. (2) The same as SPI1s, the consistency between SPI3s and SPI3g is good for most of the grid points at the 3-months scale, those grid points with the lowest were concentrated in the middle stream and downstream of the Lancang River Basin. (3) At the 1-month scale and 3-months scale, CSI ranged from 50% to 76% for most of the grid points, which demonstrated high accuracy of TMPA in drought monitoring. (4) At the 3-months scale, based on TMPA basin-wide precipitation estimates, though we tended to overestimate (underestimate) the peaks of dry or wet events, SPI3s detected successfully the occurrence of them over the five sub-basins at the most time and captured the occurrence and development of the two severe droughts happened in 2006 and 2009. This analysis shows that TMPA has the potential for drought monitoring in data-sparse regions.     

A dimension-independent extrusion algorithm using generalised maps

One solution to the integration of additional characteristics, for example, time and scale, into GIS data sets is to model them as extra geometric dimensions perpendicular to the spatial ones, creating a higher-dimensional model. While this approach has been previously described and advocated, it is scarcely used in practice because of a lack of high-level construction algorithms and accompanying implementations. We present in this paper a dimension-independent extrusion algorithm permitting us to construct from any (n–1)-dimensional linear cell complex represented as a generalised map, an n-dimensional one by assigning to each (n–1)-cell one or more intervals where it exists along the nth dimension. We have implemented the algorithm in C++11 using CGAL, made the source code publicly available and tested it in experiments using real-world 2D GIS data sets which were extruded to construct up to 5D models.     

A comparison of neighbourhood relations based on ordinary Delaunay diagrams and area Delaunay diagrams: an application to define the neighbourhood relations of buildings

ABSTRACT

The aim of this article is to describe a convenient but robust method for defining neighbourhood relations among buildings based on ordinary Delaunay diagrams (ODDs) and area Delaunay diagrams (ADDs). ODDs and ADDs are defined as a set of edges connecting the generators of adjacent ordinary Voronoi cells (points representing centroids of building polygons) and a set of edges connecting two centroids of building polygons, which are the generators of adjacent area Voronoi cells, respectively. Although ADDs are more robust than ODDs, computation time of ODDs is shorter than that of ADDs (the order of their computation time complexity is O(nlogn)). If ODDs can approximate ADDs with a certain degree of accuracy, the former can be used as an alternative. Therefore, we computed the ratio of the number of ADD edges to that of ODD edges overlapping ADDs at building and regional scales. The results indicate that: (1) for approximately 60% of all buildings, ODDs can exactly overlap ADDs with extra ODD edges; (2) at a regional scale, ODDs can overlap approximately 90% of ADDs with 10% extra ODD edges; and (3) focusing on judging errors, although ADDs are more accurate than ODDs, the difference is only approximately 1%.     

Contribution of pits dug by goannas (Varanus gouldii) to the dynamics of banded mulga landscapes in eastern Australia

The densities of pits made by goannasVaranus gouldiiwere estimated in the three distinct zones of banded mulga landscapes (erosion slope, interception zone, and mulga grove) in paddocks of a grazing study in north-western New South Wales, Australia. In light and moderately grazed paddocks, soil pits were significantly more abundant in the interception zones (M=119.057 m⁻²) than in the groves and erosion slopes (M=16.057 m⁻²). In the overgrazed paddock there were no differences in densities of pits in any of the zones. In the groves and erosion slopes approximately 70-80% of the pits contained litter, seeds, and fruits. However, on the erosion slopes less than 20% of the pits contained litter and seeds. The data support the hypothesis that soil disturbance byVaranuslizards produces a positive feedback mechanism for the viability of the interception zone and the functioning of banded vegetation landscapes.     

Badger (Taxidea taxus) disturbances increase soil heterogeneity in a degraded shrub-steppe ecosystem

In the western United States, overgrazing, weed invasion and wildfire have resulted in the conversion of shrub-steppe to annual grasslands, with substantial effects on ecosystem function. In these landscapes, badgers disturb large areas of soil while foraging for fossorial animals. Mounds created by badgers contained the lowest concentrations of total carbon, nitrogen and sulphur, mineral nitrogen and mineralizable nitrogen, inter-mound soils had the highest concentrations, and excavation pits had intermediate levels. Soil C:N ratio and pH were greater, and electrical conductivity and soluble Ca²⁺, Mg²⁺ and K⁺ were lower on mound soils compared with either pit or inter-mound soils. Larger pits generally trapped more litter, and increased litter mass equated with greater concentrations of active carbon, but only at the burned sites. Older mounds supported more vascular plants and cryptogamic crusts. Our results demonstrate reduced levels of nutrients and a higher C:N ratio on the mounds compared with either the pits or inter-mounds. Alteration to the homogeneous post-fire landscape by badgers contributes to patchiness in soils and vegetation, which is critical to the functioning of arid systems. Given their effect on soil C:N ratios, mounds may be important sites for recovery of indigenous shrub-steppe plant species.     

Access to electricity for all and the role of decentralized solar power in sub-Saharan Africa

ABSTRACT

Several hundred million people in sub-Saharan Africa are at risk of being without access to electricity by 2030 despite the ongoing work aimed at achieving the United Nations’ Sustainable Development Goal 7—sustainable energy for all. Based on qualitative interviews, quantitative surveys, observation, and participation in Kenya and Senegal between 2010 and 2018, the author aims to explain why the rate of progress is slower than intended. The findings highlight three strong hindrances: lack of affordability due to extreme poverty, mismatch between the village-level outreach of grid-based electricity systems and the geography of settlements, and lack of consideration given to gender inequality. The findings also provide new insights into the ways that decentralized small-scale solar power increasingly meets some of the shortcomings of centralized grids in addressing these three issues, even in rural areas where grids are already present. The author concludes that the ways in which village communities have combined the use of solar-powered energy with grid-supplied energy indicates the importance of the former and the need to intensify efforts to improve solar power delivery models and integrate them fully in energy sector strategies.     

Predicting human mobility with activity changes

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R²) derived from the data with and without implementing the activity change detection. The results show that the R² is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.