Based on a review on the evolution of GIS, this paper proposes a new concept namely GeoControl, which represents a new stage in the coming development of GIS. GeoControl is a process to adjust and change the movement and the state of a controlling object based on the dynamic features of its geographical environment with the support of GIS technology integrated with the others. It extends the geospatial dimensional factor within a complicated controlling system. Two good examples of GeoControl's applications are the use of GIS in UAV controlling and landing as well as in Self-Driving Car navigation. In addition, this paper introduces the concept of Live Geography proposed by Dr. Bernd Resch and redefines it as an integration of Sensor Web with GIS, which makes it possible for capturing, processing and analyzing geographical data in real-time. Both GeoControl and Live Geography will have a profound influence on the development of GIS and offer wildly application possibilities in smart city and the internet of things, promoting the role of GIS in the information society.
The gridding computation is a major model in current geoscientific research due to its simplicity in organizing data resources. However, because the gridding computation equally distributes computational resources, it brings redundancy to the computational process and neglects catastrophe points in geoscientific phenomena, which might overlook the important patterns and bring more uncertainties to the research result. To overcome this weakness, this paper proposes to use the spatial point process model in geoscientific research. The spatial point process model is used to model spatial point based geoscientific phenomenon, also is applied to most of the other geoscientific processes (because they can be transformed into spatial point processes). In this regard, the spatial point process is not only a data model, but also an analysis tool for geoscientific problems. Moreover, it provided a new angle of view for observing geoscientific problems. To extract patterns from point process data, the authors propose the frame of multilevel decomposition of spatiotemporal point process. This frame is similar to the basic idea of signal decomposition. We first assume that any point data set is the overlay of an unknown number of homogeneous point processes. Then, the points are transformed into a mixture probability density function of the Kth nearest distance of each point. After that, the optimization method is used to separate clustering points from noise. Finally, the patterns are extracted using the density connectivity mechanism. The theory can be used to any type of point process data. It can be considered as the "Fourier transform" of point process data.
Over the past 30 years, crime geography in China has set out, as the demand for crime prevention increased very rapidly. In order to get an objective and comprehensive understanding of the research development in China, a statistical analysis on crime geography studies was conducted. Taking periodical papers selected from Chinese National Knowledge Infrastructure (CNKI), WANFANG DATA, and VIP Database as samples, we retrospect the changes of contents and researchers on crime geography during three periods: 1986~1990, 1991~2000, and 2001~2010. The results show that periodical papers, research organizations and related researchers have expanded greatly in number since mid-1980s, indicating a promising tendency. Core authors, who can motivate more researchers, have emerged in four main research aspects of crime geography: urban crime geography, PGIS, CPTED, and Situational Crime Prevention. In 21st century, both new opportunities and challenges come out for crime geography. While more and more mathematical models are employed, the percentage of quantitative researches has declined significantly. It is also the same for case studies. What's more, the study areas constricts to Yangtze River Delta, Beijing-Tianjin region, and Pearl River Delta. With emerging research groups and declining share of core authors, crime geography in China has entered a phase of reorganization. In the future, much more efforts will be needed for the further development of crime geography. We should not only try to synthesize multiple theories and conduct more case studies in the Chinese context, but also pay attention to the unification of research norms and cultivation of next-generation core authors.
In the global change area, the volume of and the variety of accumulated observation and simulation data are expanding rapidly, creating challenges for the archiving, analyzing and sharing. To address these issues, this paper suggests four research strengths towards a research cyberinfrastructure in this area: scientific data, research process, interdisciplinary study, and national and international collaboration. Research on these aspects will lead to an integrated conceptual data model, an integrated research process model, a data processing module library, a data analysis service library, and a tree of standards and specifications. A research pilot project of GlobalChangeOne is highlighted to demonstrate the feasibility and the advantages of the proposed research roadmap. This research is very promising in facilitating the interdisciplinary studies and accelerating the scientific insights in the global change area, and in promoting the establishment of an integrated international platform for global change studies and sustainable development research.
In order to reveal the scale effect characteristics of DEM and slope grid with different size in hilly areas, the paper uses the typical hilly areas in Chuanshan County in Suining City as the pilot-area. Multi-scale DEM and slope with the resolution of 1m, 2.5m, 5m, 10m, 15m, 20m, 25m and 30m are generated by using contours, elevations, rivers and other data from 1:10000 scale maps. The qualitative scale effect of elevation and slope is revealed by comparing the frequency distribution of multi-scale elevation and slope. The concept of standard frequency error for testing the loss or error of accuracy created by scaling is proposed here. The correlation between altitude error(y) and scale(x) is positively significant at 0.01 confidence level, and its coefficient is 0.863. The model: y=-0.0004x2+0.0058x-0.0042 can be used to significantly describe the quantitative scale effect of altitude at 0.01 confidence level. The correlation between slope error(y) and scale(x) is positively significant at 0.01 confidence level, and its coefficient is 0.855. The model: y=-0.0001x2+0.0032x-0.0025 can be used to significantly describe the quantitative scale effect of slope at 0.01 confidence level. The research has important implication for selecting the optimal scale in specified terrain analysis of hilly areas.
Near-surface air temperature is an important controlling parameter for land surface processes, and is critical to ecological, environmental and hydrological modeling. Temperature records observed at meteorological stations have been widely used, but there has been an increasing need for temperature data in grid for modeling purposes. Although grid temperature can be estimated from in-situ temperature records using interpolation algorithm, low accuracy have been reported due to limited ground stations and their clustering distribution, especially when there were insufficient sites to represent all land cover types and terrain conditions in the area. NCEP/NCAR reanalysis project uses a frozen state-of-art global data assimilation system and a database as complete as possible. Although the NCEP/NCAR data has a coarse resolution (0.5 degree), it provides global, consistent, and long term estimation of climate variables. This paper presents a downscaling approach to derive monthly temperature at 1km resolution from the NCEP/NCAR by utilizing derived relationships between monthly aggregated NCEP/NCAR temperature and other ground elements, i.e., terrain, vegetation and geographic locations. Regression tree model was chosen to detect the possible relationships. Monthly temperature with 1km resolution for China land area from 2000 to 2010 has been produced using the approach. The final predicted temperatures were compared with observed records at 380 meteorological stations in China. The results indicate that the downscaled estimations can represent spatial distribution and trends and the magnitude of inter-month temperature with R2 ranging from 0.861 to 0.95, and RMSE from 1.88℃ to 2.681℃.
Land use and climate change under emission scenarios and their hydrological responses are some most concerned issues in watershed management. By coupling a land use land change model, Dyna-CLUE, and a hydrological model, SWAT, this paper predicts land use changes and hydrological responses in the upper and middle reaches of Heihe River Basin (HRB) under two emission scenarios, A1B and B1, from the Intergovernmental Panel on Climate Change (IPCC). After calibration, the models validate their applicability in the study area. Scenario analysis shows no obvious land use changes in the upper and middle reaches of HRB with both emission scenarios while with a same scenario, land use changes present regional characteristics. In terms of future hydrological responses, if we only consider the climate change, comparing to the reference scenario that is created with historical data of 1990~2009, mean annual streamflow in the period of 2011~2030 shows a slight decrease in the upper reaches of HRB and an obvious increase in the middle reaches under the high emission scenario (A1B), and an obvious decrease and a slight decrease respectively under the low emission scenario (B1). Under a same scenario, hydrological responses present regional characteristics in the upper and middle reaches. As climate change also causes land use change which in turn impacts hydrological responses, we compare the joint impact case with the case of climate change only. Mean annual streamflow in the period of 2011~2030 becomes less in the upper reache and greater in the middle reache under the high emission scenario (A1B), and less in both reaches under the low emission scenario (B1). The analysis indicates that climate and land use changes under emission scenarios in the study area will lead to hydrological and water resources changes and the land use change may intensify or weaken hydrological responses caused by climate change.
In order to resist the effect of outliers on DEM construction, a robust multiquadric method (MQ-R) has been developed. MQ-R firstly takes the estimation of the classical MQ as the initial values to compute the residuals of all sampling points, and then a weighted function has been constructed to determine the weights of sampling points based on the above residuals. Finally, a iteratively re-weighted MQ is formed to decrease the effect of outliers on DEM construction. At the same time, the smoothing parameter of MQ and MQ-R is determined based on a k-fold cross-validation. A synthetic surface was employed to comparatively analyze the estimation accuracies of MQ-R and the classcial MQ, where the sampling points are contaminated by three groups of errors with different distributions. These include the standard normal distribution, contaminated normal distribution with the contaminating proportion of 10%, 20% and 30%, and Cauchy distribution. Numerical tests indicate that when sampling errors are from the standard normal distribution, the accuracy of MQ-R is comparative to that of MQ. As the contaminating proportion increases, the accuracy of MQ becomes lower, whereas MQ-R can resist oultiers very well. When sampling errors are from Cauchy distribution, the results of MQ are completely destroyed, but those of MQ-R are still satisfactory. In conclusions, MQ-R with a high efficiency and a high robustness can be used to resist outliers in DEM construction.
Spatial optimization modeling for multi facilities in urbanized area is a practical and key technique, and it can provide balance configuration optimization and spatial decision support for urban public resource. A method of particle swarm spatial optimization modeling for multi facilities location based on network Voronoi diagram heuristic is proposed in this paper, in which we presented respectively some p-median location models and maximal covering location models by using ordinary Voronoi diagram heuristic and network Voronoi diagram heuristic. Those models can quantitatively extract the demands coved by the function and service of facilities through the Voronoi diagrams, and inspire spatial optimization to maximize the coverage for distributed demands by minimizing overlapped coverage. The proposed p-median location model considers the factor of demand attenuation with path distance, and the proposed maximal covering model takes it into account that facility's service provides full coverage for the demands within maximal coverage radius and partial attenuation coverage for the demands without maximal coverage radius. The genetic evolution mechanism and the dynamic neighborhood structure of particles simulated by ordinary Voronoi diagram are integrated in the particle swarm spatial optimization to improve global search and optimization performance of the algorithm. Through the research of spatial optimization configuration experiments for multi facilities in experimental city, the proposed method has been verified to be the effective and practical, it can be applied for the spatial location optimization decision in urbanized area.
Ordinary Euclidean distance is often used to measure similarity in fuzzy C-means, and in distance formula, different attribute features should have different weights according to their important degree. Moreover, for geospatial objects, clustering should consider not only similarity of attribute features, but also spatial proximity of the objects. Based on ordinary Euclidean distance, several forms of spatial weighted distance are proposed in this paper. Different distance formula imposes different weight on both two coordinate directions and each attribute feature. The weight vector is used to measure effect sizes of spatial location features and attribute features in similarity-based clustering and also measure degree of isotropy and anisotropy along X and Y coordinate directions. A fuzzy evaluation function derived from similarity matrix of spatial objects is used as optimization objective, and the weight vector is learned by gradient-descent algorithm based on dynamic learning rate. Then, spatial weighted distance is introduced to fuzzy C-means clustering to replace ordinary Euclidean distance. Meuse dataset, a spatial dataset as the application example, is analyzed by FCM clustering and the clustering number is set to 2-10. The clustering results are evaluated and compared via cluster validity indices including PC, PE and Xie-Beni. The analysis indicates that clustering performance based on spatial weighted distance is better than ordinary Euclidean distance and spatial common distance, and further, spatial distribution of the clustering results shows that, besides attribute features, spatial features such as locations also play important roles in spatial data clustering.
The development of Internet of Things and Earth Observation brings big observation array data and big array analytics. This paradigm shift challenges the development of current GIS infrastructure which needs innovative proof and design. Array Analytics is considered as one of potential solutions to address today's data tsunami challenge. Formal definitions, such as array domain, range, the mapping function between domain and range, operators, the corresponding input and output are basic building blocks of Array Analytics theory. This paper focuses on the world-wide research progress of Array Analytics towards Big Data. Main Array Analytics models are reviewed and discussed by considering different array definition, operation functions, input and output, making it possible to explore character differentiation and summarize model expressiveness. Then, main array analytics technologies and implementations are discussed and compared by considering different models. Accordingly, array analytics modeling patterns, principle issues and directions for future research are concluded as guidance for further exploration on array analytics theory research, technology improvement and domain applications. National Digital Information Infrastructure Initiatives need effective array storage and analytics platforms. The research is conducted as input of further enhancement on Information Application Infrastructure, which is key backbone of GIS analytics services in terms of earth science system monitoring and forecasting, existing digital construction consolidation, and GIS infrastructure improvement, so as to provide effective support to digital region construction.
Watershed delineation based on DEMs in the GIS environment is a fundamental work for hydrological analysis. But the feature difference between normal and inter-watershed watershed has long been ignored. This text believes that existence of inter-watershed must be taken into account during watershed delineation and analysis. In this paper, an overall explanation of basic concepts and characteristics of inter-watershed is presented, and a method of extracting inter-watershed automatically based on DEMs is introduced. Experimental results in the Loess Plateau region show that inter-watershed could be extracted accurately and quickly after a comprehensive consideration of water accumulation threshold, terrain feature and the boundary of data. Generally speaking, flat topography may lead to the wrong position of outlets of watersheds, which can be corrected with help of rivers' DLG data. And if boundary of the data is not the dividing crest, it's difficult to determine whether a watershed whose dividing line consists of data's boundary is inter-watershed or not. Comparison of inter-watershed and normal watershed in terms of spatial form, spatial distribution and hydrological characteristics based on experimental results show that inter-watershed performs notable features. Firstly, spatial pattern of inter-watershed is clump and tends towards stability with growing minimum catchment area settled. Secondly, the amount of inter-watershed depend on the number of runoff node that means that it has a power function with minimum catchment area in the test area. And a good linear relation between average area of inter-watershed and average area of normal watershed could be found in the experiments. Thirdly, spatial shapes of inter-watershed make it collect water quickly if threshold value is settled larger than 0.4 km2 based on the statistic of shape index of the inter-watersheds extracted in the experiment area. In conclusion, the concept of inter-watershed is defined firstly and its basic characteristics are also interpreted. The method introduced contributes to ensure the correctness of watershed delineation. The study on inter-watershed is of great significance not only in fluvial geomorphology research but also the theories and methods of digital terrain analysis.
Given school bus trips for each school in a school district, if a school bus can serve multiple trips, the efficiency of school bus service can be improved in terms of the number of buses needed and the total travel cost. The school bus scheduling problem (SBSP), a class of school bus routing problem (SBRP), is concerned with assigning a fleet of buses to serve all the given trips and aims to get optimal bus schedules. Each school has its xed time window within which school bus must arrive at the destination school of the trip. In existing literatures, SBSP is usually formulated as a transportation problem (TP) or an assignment problem (AP). However, many existing algorithms for vehicle routing problem (VRP) have not been fully utilized to solve the problem effectively. This paper proposes a meta-heuristic algorithm for large-scale SBSP. Treating a trip as a virtual stop with time window, the problem can be converted to a vehicle routing problem with time windows (VRPTW). Therefore, the SBSP can be solved in a VRP algorithm framework. After a set of feasible solutions are generated using construction heuristic algorithm, a simulated annealing (SA) algorithm is designed to improve the initial solutions iteratively. Four general operators for VRP, one-point move, two point move, two-opt move and cross-exchange move, are used in the neighborhood search. In addition to the SBSP objectives of minimizing the number of the routes and the total travel distance, the sum of squared number of route stops is added as a new objective. This will guide the neighborhood search toward the situation that deleting some routes more easily. For avoiding local optimum, some worsening neighborhood solutions can be accepted with a certain probability. Computational tests on 15 instances with a homogeneous fleet show the effectiveness of the proposed approaches. Compared with the existing SBSP solutions, the proposed algorithm can solve large-scale SBSP in a reasonable time and find better solutions using fewer buses. In addition, the algorithm can be easily integrated with GIS for solving real world school bus scheduling.
With the development of earth observation technologies, sensor type and quantity increase massively. In order to manage information and service resources related to Earth Observation Sensor Web efficiently, allow users access and apply these resources comprehensively and accurately, based on Sensor Web information model and service interface specifications, this paper designs and implements geospatial sensor web Information Public Service Platform, namely GeoSensor. GeoSensor is based on a service-oriented architecture, calls and combines sensor registration service, sensor observation service, real-time mapping service, satellite location-based service and other services, gets real-time sensor information, sensor observational data, data product and other information resources and demonstrates these information resources in the Map World with graphics, text, tables, and video vividly. GeoSensor mainly consists of four major functional modules: (ⅰ) Sensor Retrieval Module which is mainly used for retrieving motion sensors, in-situ sensors and remote sensors according to the specified filter criteria, such as time, space, subject and other conditions; (ⅱ) Sensor Observational Data Retrieval Module is mainly used according to the specified filter conditions, access to various types of sensor observation data, and show these observational data in Map World in different ways; (ⅲ) Sensor Control Module is mainly used to control in-situ sensors and video sensors, and feedback the result of sensor control based on these changes of the sensor observational data or the access method of sensor observational data; And (ⅳ) Thematic Map Service is to use sensor observation data to generate thematic maps, and the data products generated by this service can reflect the overall situation within a specific area. GeoSensor achieves online search, instant access, network control and real-time mapping and other functions for these resources. In the future, a more powerful sensor web information public service platform with semantics will be build, which could be used in smart city.
The high-speed railway technology has been growing rapidly in China. At the end of December 2012, the mileage of the high-speed railway in China had been nearly 9356 kilometers. China has become the country which has the scale biggest of high-speed railway mileage in the world. Simulation of general coupling system of high-speed railway is a highly complex dynamic process. Different domain objects have complex and dynamic relations, which are uncertainty and randomness. Thus, it is very important that these simulation data are implemented analysis, reorganization and visualization in a virtual geographical environment. Based on the special spatial features and the relationship attributes of high speed railway environment, this paper proposed a modeling method to meet the higher requirements of the visualization and management of high-speed railways. Some key problems including the creating of the primitive model base, the design of the ontology knowledge base and spatial semantic constraint rules were addressed in detail to develop a modeling service system and related plug-ins, which can provide fast modeling service for virtual high speed railway. Finally, a prototype system was implemented for modeling experiments on a case study region. The experimental results show that the proposed method in this paper can efficiently clarify various relations of scenes object entities, separate the domain knowledge from the modeling operation, and reduce the complexity and difficulty of the multidisciplinary collaborative modeling. It can support fast organization and modeling of virtual high-speed railway scene.
With the increasing of spatial resolution of imaging sensors, object-oriented feature information extraction technology is developing rapidly. The advantages of object-based classification over the traditional pixel-based approach are well documented. Image segmentation is a key step to realize the object-oriented classification. The choice of scale parameter is very important and has a great influence on the segmentation effectiveness, but the choice of scale parameter is still decided by the repeated attempts and subjective judgments of operator, which are lacking in stability and reliability. Thus, an objective and unsupervised method is proposed for selecting optimal parameter for image segmentation to ensure best quality results. In this paper, WorldView 2 as data source, a new method based on principal component transform is introduced to choose an optimal parameter for image segmentation. We choose principal component images as the editor of image segmentation and eigenvalues as the weights of heterogeneity f and segmentation global score. Segmentation images, ranging from 20 to 200 scale, step by 10, are created in Definiens Professional 8.7. Then, the global intra-segment and inter-segment heterogeneity indexes are taken into account to identify the optimal segmentation scale (i. e. the highest GS value) by using the cubic spline interpolation function method. After comparison with the results of image segmentation based on traditional three bands, image segmentation effect obtained by principal component transform has obvious advantages. As a result, the method in this paper can effectively avoid the subjectivity of the artificial segmentation, one-sidedness and inefficiency, improve the quality of high-resolution image segmentation. The method also makes a good preprocessing work for later image classification and information extraction.
Chlorophyll a concentration, which can be routinely measured by ocean color remote sensing at large scale, is one of the most important indicators to evaluate water quality. The standard inversion algorithms based on empirical model, however, often overestimate chlorophyll a concentration in case Ⅱ waters. After tuning key parameters of a typical semi-analytical model called GSM01 (Garver-Siegel-Maritorena-01), multi-sensor reflectance data of East China Sea acquired on May 11, 2008, which were from Aqua MODIS, Terra MODIS and SeaWiFS, were merged together to retrieve chlorophyll a concentration. The retrieved result was compared with that of the adaptive weighted averaging method and validated by field survey data. Result showed that retrieval of chlorophyll a concentration with multi-sensor data by GSM01 merging algorithm has four advantages: (1) the range of the retrieved values with GSM01 was basically consistent with the in-situ measurements. Because the influence of water's absorption and scattering on remote sensing reflectance was taken into account by GSM01 model, overestimation of chlorophyll a concentration due to high concentrated suspended particulates near the coast was thus avoided. (2) More input bands (from original 6 for single senor to final 18 for multi-sensor in this case) were involved in the merging procedure, the freedom degree of model solution as well as the accuracy of the retrieval was thus improved. In addition, the spatial consistency of the result is ensured by minimizing root mean square error between the measured values and the retrieved values from different remote sensing data source. (3) Instead of merging the chlorophyll a concentration data, which were input of the adaptive weighted averaging method, the GSM01-based method directly merges the original ocean color remote sensing reflectance data, which can better prevent the propagation of error. And (4) the GSM01-based method shows more flexibility because the input reflectance bands can be specified as required.
Wetlands are considered as one of the most productive ecosystems on earth, and more vulnerable and sensitive compared to other ecosystems. With the development of the society, wetlands are suffering serious degradation and loss due to intense anthropogenic disturbance, such as wetland pollution, land use, etc. As one of the effective management measurements, regionalization of wetland function is helpful to have resources in a sustainable development pattern. This paper takes Widgeon Lake, in Beijing, as study region, combining with multi-source remote sensing data, wetland investigation, and the field and meteorological observation data, to analyzes natural features of wetland surrounding, rare feature of wetland type, rare feature of wetland species and the impact of human activities on the wetland. On the other hand, the species and species distribution, soil type and soil moisture, vegetation type and vegetation growing feature, soil erosion and land cover are analyzed, in order to implement regionalization of wetland. Consequently, based on the principles of landscape ecology and wetland science, the function division is conducted, and the leading function area is determined based on GIS technique and overlay analyses, correlation analyses, and leading index methods. As to the management district, according to the hydrological situation and the DEM, the supplement region and protection region are marked out firstly. Then the center, buffer and experiment regions are ascertained in the protection region. As to the leading function area, the protection region is divided into bird protection, vegetation protection, water environment, material supplement, recreation and service regions.
