Quantification of spatial structure reveals the distribution patterns of geographic features, which is essential for geographic analysis. For quantitative measurements of multivariate spatial structure, existing methods often neglect either the geographic meaning or the spatial combination of the multivariate dataset. In this paper, a new indicator for multivariate spatial structure (MuSS) is proposed. Since multivariate datasets characterize geographic conditions, the correlation between multivariate attributes at different locations can be measured as the similarity of geographic conditions. The MuSS indicator evaluates whether location pairs with closer distances have higher geographic similarities. Experimental results show that MuSS outperforms existing methods in differentiate multivariate datasets with varied spatial distribution patterns. A MuSS value deviating from 1 suggests that geographic similarity between location pairs is relevant to their distance, and the statistical significance of the captured distribution patterns is evaluated using a p-value from a permutation test. MuSS is also applied to real geographic data at different spatial resolutions in two study areas with diverse distribution patterns. Case studies show that MuSS provides consistent comparison results for spatial structure levels between study areas, while existing methods cannot.

Parallel programming libraries have been proposed to simplify programming for parallel raster-based geocomputation through hiding parallel programming details for users. However, the strategy of data domain decomposition used in existing libraries often leads to load imbalance owing to inherent characteristics of geocomputation including not only irregular spatial data distribution, but also spatial variation in the amount of computation, thereby impeding their parallel performances. This paper thus proposes a load-balancing strategy of data domain decomposition in parallel programming libraries for raster-based geocomputation based on the concept of spatial computational domain, which characterizes the distribution of computational intensity based on geocomputation characteristics. By implementing the proposed strategy with the message passing interface (MPI), a set of parallel raster-based geocomputation operators across different parallel computing platforms (known as PaRGO V2) was upgraded to improve load-balancing parallelization. The proposed strategy was evaluated by parallelizing two typical geocomputation algorithms (i.e. inverse distance weight interpolation and fuzzy c-means clustering) using PaRGO V2 with uneven distributed computational intensity. The results show that the proposed strategy with PaRGO V2, compared with the previously adopted data domain decomposition strategy, yielded significant improvements to the load balance (i.e. better parallel performance).

Due to errors in sensors and positioning, there exist mismatches between different phases of mobile laser scanning point clouds, which impede the application of point cloud, such as changing detection and deformation monitoring. To rectify such mismatches, we designed a 3-D deep feature construction method for point cloud registration. The proposed method combines two 3-D convolutional neural networks into a uniform deep learning model to extract 3-D deep features. First, the corresponding points and noncorresponding points are set to train the deep learning model to minimize the distance between corresponding points’ features and maximize the distance between features of noncorresponding points. Second, in the test phase, the 3-D deep feature for each keypoint was extracted by the trained deep learning model. This could be used to determine the corresponding points by the$k$-dimensional tree and random sample consensus (RANSAC) algorithm. Finally, a transformation matrix was calculated based on the corresponding points and was then applied to point cloud registration. The experimental results illustrated that the proposed method of using 3-D deep features is more efficient at a corresponding point search than representatives of three existing methods. It also improved registration accuracy.

O2O (online to offline) service recommendation is a typical context-aware service application, which needs to provide the most suitable services to customers in time according to their user profile and current context. By means of composing various data sources, different O2O service recommendation strategies can be customized, which may lead to great performance difference. Incorrect or non-real-time service recommendation would not work well and even cause negatives consequences. As a result, how to evaluate the performance of different O2O service recommendation strategies and select the most suitable one has become a key problem in the field. Due to the diversity and the variability of context events, as well as the economic, legal, and ethical impact, it is difficult or even impossible for traditional methods to realize comprehensive evaluation of various service strategies. Based on the background, this paper proposes a computational experiment-based evaluation method of O2O service recommendation strategies, which mainly consists of three parts: customization of O2O service strategies, modeling of experiment system, and execution of experiment evaluation. As a case study, the method was applied to Food O2O service. Three kinds of service strategies were compared respectively under two different market environments. Experiment results show that the proposed evaluation method is effective.

The vast accumulation of environmental data and the rapid development of geospatial visualization and analytical techniques make it possible for scientists to solicit information from local citizens to map spatial variation of geographic phenomena. However, data provided by citizens (referred to as citizen data in this article) suffer two limitations for mapping: bias in spatial coverage and imprecision in spatial location. This article presents an approach to minimizing the impacts of these two limitations of citizen data using geospatial analysis techniques. The approach reduces location imprecision by adopting a frequency-sampling strategy to identify representative presence locations from areas over which citizens observed the geographic phenomenon. The approach compensates for the spatial bias by weighting presence locations with cumulative visibility (the frequency at which a given location can be seen by local citizens). As a case study to demonstrate the principle, this approach was applied to map the habitat suitability of the black-and-white snub-nosed monkey (Rhinopithecus bieti) in Yunnan, China. Sightings of R. bieti were elicited from local citizens using a geovisualization platform and then processed with the proposed approach to predict a habitat suitability map. Presence locations of R. bieti recorded by biologists through intensive field tracking were used to validate the predicted habitat suitability map. Validation showed that the continuous Boyce index (Bcont(0.1)) calculated on the suitability map was 0.873 (95% CI: [0.810, 0.917]), indicating that the map was highly consistent with the field-observed distribution of R. bieti. Bcont(0.1) was much lower (0.173) for the suitability map predicted based on citizen data when location imprecision was not reduced and even lower (−0.048) when there was no compensation for spatial bias. This indicates that the proposed approach effectively minimized the impacts of location imprecision and spatial bias in citizen data and therefore effectively improved the quality of mapped spatial variation using citizen data. It further implies that, with the application of geospatial analysis techniques to properly account for limitations in citizen data, valuable information embedded in such data can be extracted and used for scientific mapping.

The demand for parallel geocomputation based on raster data is constantly increasing with the increase of the volume of raster data for applications and the complexity of geocomputation processing. The difficulty of parallel programming and the poor portability of parallel programs between different parallel computing platforms greatly limit the development and application of parallel raster-based geocomputation algorithms. A strategy that hides the parallel details from the developer of raster-based geocomputation algorithms provides a promising way towards solving this problem. However, existing parallel raster-based libraries cannot solve the problem of the poor portability of parallel programs. This paper presents such a strategy to overcome the poor portability, along with a set of parallel raster-based geocomputation operators (PaRGO) designed and implemented under this strategy. The developed operators are compatible with three popular types of parallel computing platforms: graphics processing unit supported by compute unified device architecture, Beowulf cluster supported by message passing interface (MPI), and symmetrical multiprocessing cluster supported by MPI and open multiprocessing, which make the details of the parallel programming and the parallel hardware architecture transparent to users. By using PaRGO in a style similar to sequential program coding, geocomputation developers can quickly develop parallel raster-based geocomputation algorithms compatible with three popular parallel computing platforms. Practical applications in implementing two algorithms for digital terrain analysis show the effectiveness of PaRGO.

This article presents a set of artificial surfaces simulating complex terrain types for evaluating the performances of grid-based flow direction algorithms. The proposed artificial surfaces were developed based on sine and cosine functions and thus can simulate four complex terrain types: a convex-centred slope, concave-centred slope, saddle-centred slope and straight-ridge slope; such features are typical and widespread in real landscapes. We analytically solved the theoretical values of specific catchment area (SCA) for the proposed artificial surfaces. Compared with existing artificial surfaces for evaluating flow direction algorithms, the proposed artificial surfaces provide a better representation of common terrain types in the real world. To analyse the feasibility of the proposed artificial surfaces, two sets of artificial digital elevation models (DEMs) were created by sampling the proposed artificial surfaces with different reliefs at a series of resolutions (i.e. 1, 5, 10 and 20 m). Four representative flow direction algorithms were applied to these artificial DEMs: D8, D-inf, FD8 and MFD-md. The root mean square error, mean error and standard deviation in the computed SCA from flow direction algorithm show that MFD-md generally yielded lower error under simulated terrain conditions than D8, D-inf and FD8. The cumulative frequency distributions of errors from the tested flow direction algorithms with the proposed artificial DEMs can effectively reflect the inherent characteristics of each algorithm. MFD-md performed more similarly to FD8 in low-relief terrains and more similarly to D-inf in high-relief terrains. The map of errors from each tested algorithm is available for a spatially explicit evaluation of the occurrence of errors. Over most of the area, the D-inf algorithm underestimated the SCA when FD8 overestimated the SCA.

Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km2) and the other for model extrapolation (approximately 4600 km2). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.

Sampling design plays an important role in spatial modeling. Existing methods often require a large amount of samples to achieve desired mapping accuracy, but imply considerable cost. When there are not enough resources for collecting a large set of samples at once, stepwise sampling approach is often the only option for collecting the needed large sample set, especially in the case of field surveying over large areas. This article proposes an integrative hierarchical stepwise sampling strategy which makes the samples collected at different stages an integrative one. The strategy is based on samples' representativeness of the geographic feature at different scales. The basic idea is to sample at locations that are representative of large-scale spatial patterns first and then add samples that represent more local patterns in a stepwise fashion. Based on the relationships between a geographic feature and its environmental covariates, the proposed sampling method approximates a hierarchy of spatial variations of the geographic feature under concern by delineating natural aggregates (clusters) of its relevant environmental covariates at different scales. The natural occurrence of such aggregates is modeled using a fuzzy c-means clustering method. We iterate through different numbers of clusters from only a few to many more to be able to reveal clusters at different spatial scales. At a particular iteration, locations that bear high similarity to the cluster prototypes are identified. If a location is consistently identified at multiple iterations, it is then considered to be more representative of the general or large-scale spatial patterns. Locations that are identified less during the iterations are representative of local patterns. The integrative stepwise sampling design then gives higher sampling priority to the locations that are more representative of the large-scale patterns than local ones. We applied this sampling design in a digital soil mapping case study. Different representative samples were obtained and used for soil inference. We started with samples that are the most representative of the large-scale patterns and then gradually included the samples representative of local patterns. Field evaluation indicated that the additions of more samples with lower representativeness lead to improvements of accuracy with a decreasing marginal gain. When cost-effectiveness is considered, the representative grade could provide essential information on the number and order of samples to be sampled for an effective sampling design.

In a spatio-temporal data set, identifying spatio-temporal clusters is difficult because of the coupling of time and space and the interference of noise. Previous methods employ either the window scanning technique or the spatio-temporal distance technique to identify spatio-temporal clusters. Although easily implemented, they suffer from the subjectivity in the choice of parameters for classification. In this article, we use the windowed kth nearest (WKN) distance (the geographic distance between an event and its kth geographical nearest neighbour among those events from which to the event the temporal distances are no larger than the half of a specified time window width [TWW]) to differentiate clusters from noise in spatio-temporal data. The windowed nearest neighbour (WNN) method is composed of four steps. The first is to construct a sequence of TWW factors, with which the WKN distances of events can be computed at different temporal scales. Second, the appropriate values of TWW (i.e. the appropriate temporal scales, at which the number of false positives may reach the lowest value when classifying the events) are indicated by the local maximum values of densities of identified clustered events, which are calculated over varying TWW by using the expectation-maximization algorithm. Third, the thresholds of the WKN distance for classification are then derived with the determined TWW. In the fourth step, clustered events identified at the determined TWW are connected into clusters according to their density connectivity in geographic–temporal space. Results of simulated data and a seismic case study showed that the WNN method is efficient in identifying spatio-temporal clusters. The novelty of WNN is that it can not only identify spatio-temporal clusters with arbitrary shapes and different spatio-temporal densities but also significantly reduce the subjectivity in the classification process.

Most multiple‐flow‐direction algorithms (MFDs) use a flow‐partition coefficient (exponent) to determine the fractions draining to all downslope neighbours. The commonly used MFD often employs a fixed exponent over an entire watershed. The fixed coefficient strategy cannot effectively model the impact of local terrain conditions on the dispersion of local flow. This paper addresses this problem based on the idea that dispersion of local flow varies over space due to the spatial variation of local terrain conditions. Thus, the flow‐partition exponent of an MFD should also vary over space. We present an adaptive approach for determining the flow‐partition exponent based on local topographic attribute which controls local flow partitioning. In our approach, the influence of local terrain on flow partition is modelled by a flow‐partition function which is based on local maximum downslope gradient (we refer to this approach as MFD based on maximum downslope gradient, MFD‐md for short). With this new approach, a steep terrain which induces a convergent flow condition can be modelled using a large value for the flow‐partition exponent. Similarly, a gentle terrain can be modelled using a small value for the flow‐partition exponent. MFD‐md is quantitatively evaluated using four types of mathematical surfaces and their theoretical ‘true’ value of Specific Catchment Area (SCA). The Root Mean Square Error (RMSE) shows that the error of SCA computed by MFD‐md is lower than that of SCA computed by the widely used SFD and MFD algorithms. Application of the new approach using a real DEM of a watershed in Northeast China shows that the flow accumulation computed by MFD‐md is better adapted to terrain conditions based on visual judgement.

When two spatial point processes are overlaid, the one with the higher rate is shown as clustered points, and the other one with the lower rate is often perceived to be background. Usually, we consider the clustered points as feature and the background as noise. Revealing these point clusters allows us to further examine and understand the spatial point process. Two important aspects in discerning spatial cluster features from a set of points are the removal of noise and the determination of the number of spatial clusters. Until now, few methods were able to deal with these two aspects at the same time in an automated way. In this study, we combine the nearest‐neighbour (NN) method and the concept of density‐connected to address these two aspects. First, the removal of noise can be achieved using the NN method; then, the number of clusters can be determined by finding the density‐connected clusters. The complexity for finding density‐connected clusters is reduced in our algorithm. Since the number of clusters depends on the value of k (the kth nearest neighbour), we introduce the concept of lifetime for the number of clusters in order to measure how stable the segmentation results (or number of clusters) are. The number of clusters with the longest lifetime is considered to be the final number of clusters. Finally, a seismic example of the west part of China is used as a case study to examine the validity of our method. In this seismic case study, we discovered three seismic clusters: one as the foreshocks of the Songpan quake (M = 7.2), and the other two as aftershocks related to the Kangding‐Jiulong (M = 6.2) quake and Daguan quake (M = 7.1), respectively. Through this case study, we conclude that the approach we proposed is effective in removing noise and determining the number of feature clusters.