The research purpose of the spatialization of population data is to capture the size and the distribution location of population in the geographical space. It plays an important role in presenting the geographical meaning of demographic data. Spatializing the statistical population data has increasingly become a research hotspot in the fields of demography, geography and GIS. Population distribution dataset is a key achievement in the spatialization study. At present, there are a few widely-used population distribution datasets and influential population spatialization projects, including GPW/GRUMP, LandScan and UNEP/GRID & China km grid population datasets. Population distribution dataset has practical application values and the scientific significance for relevant researches, such as government planning and decision making at all levels, disaster assessment and resource allocation. After nearly 30 years' development, the spatialization researches are evolving into the maturity stage. They have obtained many achievements and produce a rich variety of spatialization models of population data. Based on the purpose of spatializing census data and the differences between modeling concept and model principle, this paper reviews the spatialization methodologies in three major aspects: (1) the method and characteristic of the selection of grid size (scale); (2) 3 types of common adopted modeling ideas and a comparative analysis between 6 types of basic models; and (3) the proper strategies used for improving the simulation accuracy and their application background and advantages. Finally, according to the research contents of population data spatialization at present stage, this article discusses the further study direction through four perspectives: (1) the suitability of grid size; (2) the simulation of spatial distribution of population at high spatial and temporal resolution; (3) the adoption of new type of data source; and (4) the comprehensive application of multi-thought and multi-model. It is significant to grasp the current status of spatialization research and promote the further development of spatialization methodologies.
The volunteered geographic information is mostly derived from volunteers' uploading with no quality guarantee. This has become a major obstacle of the application for the volunteered geographic information. Also, the quality becomes the primary problem which requires to be solved firstly. The quality evaluation is the focus of the present research domain. There are many researches on the quality evaluation of volunteered geographic information but a few research on the quality evaluation without reference data. Since it is very difficult or costly to get the reference data, it is important to study the quality evaluation without the reference data. In order to solve this problem with an unknown quality of the volunteered geographic information and the difficulty of getting the reference data with high quality, we proposed the credibility model. The credibility model could evaluate the VGI quality from the number of the volunteers and their reputation for the data changing trend. Then, we turn the qualitative analysis result into the quantitative expression. On one hand, the volunteer's reputation model was built based on this point, meaning a statistic method which takes the proportion for the preserving points as the volunteer's reputation value. Then, the Linus Law adapts and measures the geographical information collected by volunteers, of which credibility relied on the sum of the volunteer's reputation within the research areas. On the other hand, the information quality is gained from the data-changing tendency and measured by the geographical information credibility through computing the degree of change within the research areas. At last, for verification and analyzing the rationality of the credibility model, the OpenStreetMap, collects previous data adapted for Beijing, Shanghai and other cities which require to be used in experiments. Finally, the navigation data is selected as the reference data for the comparison. The result of calculation for the credibility model has a great coherence for the result based on the reference data.
Similarity relation is one of the focal spatial relations in the community of geographic information science and cartography. The spatial similarity calculation in multi-scale map spaces is a research hot spot in Geographic Information Systems (GIS). Point cluster object contains plenty of structured information in its spatial distribution. Its similarity is widely used in the retrieval and query of spatial databases and is also used to analyze and process the spatial data, to recognize the spatial objects from image and to describe the spatial features on maps. Point clusters can be taken as a simple spatial object in geographic space and with studying its similarity we are able to evaluate the result of computer drawing and to calculate complex clusters' similarity, such as the spatial line clusters, the spatial polygon groups and a mixture of points, lines and polygons. Previous theoretical researches mainly focus on a single factor that could impact the point group target, then analyze the impact factor of the point clusters, and in the end, carry out a calculation model without considering the effect of mixing factors. However, so far these researches have hardly made any significant achievements. In this paper, with the consideration of the Gestalt principles from visual cognition, incorporating predecessors' research results, a calculation model is proposed to comprehensively grasp the point clusters similarity in detail. In order to calculate the similarity between different point clusters in the multi-scale map spaces, the main factors that could affect the similarities of point cluster objects were integrated, including the topological relation, the distribution range, the direction relation, the distance relation and the distribution density. Then, this paper discusses the calculation methods of the topological relation, direction relation, distance relation, distribution range and distribution density for point clusters in the multi-scale map spaces. According to the calculations of the five factors, this paper describes the topological relation using the concept of topological neighbor, represents the distribution range by stripping the outside triangles after triangulation, uses the trend of main skeleton for point clusters to express the direction relation, indicates the distance relation by calculating the mean distance between each point and the distribution center for each point cluster, and expresses the distribution range by the overall relative density. Their complete similarity calculation models were put forward respectively at the same time. Analytic Hierarchy Process (AHP) analysis method was adopted for weight assignment, which is a qualitative and quantitative method and can be systematic. Hierarchical analysis method of weighting factor was integrated to address the impact of weight problem. It only uses a small amount of quantitative information, with the help of mathematical methods, complex issues can be simplified. The importance of different factors were taken into account, and the topological relation weight, the direction relation weight, the distance relation weight, the distribution range weight and the distribution density weight were calculated. Finally, the integrated similarity calculation model with the influential factors' weights for point clusters in multi-scale map spaces was established. The validation results of an example shows that the model can accurately calculate the spatial similarity of point clusters in multi-scale map spaces, meanwhile the model is proved to be feasible and effective , which can be applied to evaluate the quality of map generalization.
The limitations of expressing faults, the interpolation dilemma and other problems exist in the 3D modeling and the visualization technique of geological faults. Through our research, the reason why these problems exist is that these methods mainly focus on the geometrical morphology of the geological faults and do not concern the formation mechanism of the geological faults. As we all know, no matter how complex the geologic bodies that containing the geological faults are, the formation mechanisms which they depend on are similar. Besides, the modeling of the geologic faults is composed of a series of processes. If we can model the faults through certain ways to express their formation mechanisms, then we may solve the above mentioned problems and limitations. Thus, we proposed an expression method to express the formation mechanism for modeling and visualizing the geological faults. Firstly, we analyzed the operands and operators needed to construct the geological fault expression, constructed the rules for the expression based on the theory of formation mechanism of the geological faults, and used the context-free grammar rules to describe the rules for fault expression based on the formation mechanism. Then, we formated the original fault exploration data to the standardize 2D data table, extracted the operators and operands from the 2D data table to construct the expression of geological faults. Finally, we calculated the expression fomular to generate the abstract faults model and achieved the three-dimensional geological modeling with the relevant exploration data based on the abstract faults model. Experiment is carried out based on DaLian River geological data which comprising a plurality of geological faults. Geological modeling results show that the expression method solves the limitations of fault expression, and avoids the interpolation dilemma accured in the geological layer fracture zone.
The research object of this paper is based on the remote sensing data sharing website (OSDS) founded by the Chinese Academy of Sciences in 2005. Using the nearest neighbor hierarchical spatial clustering method and the model of geographic detector, the spatial distribution characteristics of the registered users and the relevant influencing factors were analyzed. Analysis results show that the overall user space distribution is not balanced, which mainly aggregates in the eastern developed regions and in several areas that have outstanding achievements in the field of surveying, mapping and geographic information science. Information, scientific research and education are the main influencing factors. The influence of economic, network and mapping are low when they are considered separately as a single factor, but their interactions with the main influencing factors would improved the influence. Therefore, there are multiple factors restricting the spatial distribution pattern and regional imbalance of the user group. In this paper, the spatial distribution characteristics and the influencing factors of the user group that is consisted by the remote sensing scholars can be grasped using the geographic detector. The analysis results are helpful to the data providers to deliver services to the targeted users more efficiently, and also provide the references to the adjustment of remote sensing industry and the optimization of spatial layout.
Geological Environment Evaluation (GEE) is not only the necessary pre-condition to master the temporal-spatial distribution pattern and change trend of geological environment, but also the critical method for conducting the prevention & protection management work of geological environment. There are diverse evaluation indices and corresponding computing models for different thematic and purposeful GEE. Meanwhile, in the big data era, the geological environmental data are always characterized to be of multi-sources, heterogeneous formats and storage modes, as well as having diversified service patterns. As a result, the geological environment evaluation system (GEES) is necessary to meet the above changes and requirements. Firstly, this paper puts forward the concept model of GEE, and analyzes the general workflow of GEE. Secondly, the authors propose and design a configurable software method for GEES. Based on the core process of GEE, this method can configure not only the indices and their weights, but also the indices' data sources and data retrieving measures, indices' value dimensionless processing approach, the computing approach of evaluation value according to the evaluation spatio-temporal extent, as well as the classification and spatial visualization of the indices and evaluation results. Finally, with the support of geography information technology and Java programming language, a Configurable Geological Environment Evaluation System (C-GEES) is developed. It includes five logic tiers from the bottom up, i.e. the data tier, index tier and model tier as well as the function tier and service tier. Specially, the function tier is divided into two layers: one is the configuration layer which mainly implements the above mentioned configuration functions for system administrators, and the another is the application layer which provides lots of data querying and browsing, evaluation, and analysis functions for users. It provides the one-click or step-by-step GEE with different evaluation indices and application purposes. A verification test is conducted in Tangshan city of Hebei province, China. The result shows that C-GEES is good at configuration which makes it powerful in its expansibility and applicability.
In recent years, the urban built-up area continues to expand along with the continuous improvement of urbanization degree. Also, the property of urban land is changing. In various types of urban planning land, different urban construction plannings can cause different ecological effects. The study on the differences and the causes of their formations can provide a good decision support to urban planning managers, and it is of great significance for the construction of an ecological livable city. In this paper, two Landsat images of 2009 and 2013 were utilized to computer the Remote Sensing Ecological Index (RSEI). Combined with the newly approved Urban Master Planning of Fuzhou City by the State Council of China, five planning classes of the master planning, with 22 selected plots, were investigated to reveal their ecological changes before and after the implementation of construction planning. It is found that the ecological qualities of the five classes are all declining, which indicates an overall drop down in the RSEI value by 12%. Among them, the RSEI of the industrial class falls by 18% and the ecological degradation of the industrial class is the most sever one. At the same time, the ecological quality of the municipal utilities class falls by 14%, the ecological quality of the residential class falls by 11%, the ecological quality of the administration and public services class falls by 8%, and the ecological quality of the commercial and business facilities class falls by 6%. The main factors which lead to the degradation of ecological quality after the completion of urban planning are the significant decrease in vegetation and water, and the considerable increase in coverage for built-up impervious surface. Combining with the research results of this paper, we also put forward suggestions for the urban planning management department.
Land suitability for construction is fundamentally useful to land use planning and management. In this study, we defined a connotation of land suitability for construction based on land use sustainability, and constructed a standardized indicator system of suitability from four dimensions of disaster risk, terrain, ecological environment and location. Against the deficiency of the exiting evaluation method, we constructed an evaluation framework of multifactor-distributed algorithm and scenario matrices integrating extreme value methods, conditional function methods and linear weighted comprehensive methods. This study discerned the conflict space based on suitability and used the proportion of conflict area of total built-up area as space conflict intensity to measure the reasonability and sustainability of spatial distribution. This study took Manasi County, Xinjiang Province as a case and the results indicated that: (1) the potential suitable construction land is very sufficient, the total area suitable for construction is more than 230 km2 (around 26% of the total area of the county). The land with suitability grade 4 and 5 mainly distribute in Manasi town, Letuyi town, Farm 147, Xinhu Farm and the farms in northern area. The land with rank 3 distributes in outlying regions of land of rank over 3 and the piedmont plain in central south. (2) the space with conflicts is up to 4.21 km2, 22.74‰ of the built-up area. The spatial conflicts are mainly distributed along the Manasi River and Taxi River, in the environmental fragile zone and in the central industrialized urban area of the county as well. This case study suggested that the framework and evaluation methodology of construction land suitability proposed in this paper is viable and close to the reality. It can offset the disadvantages of existing evaluation methods for construction land suitability. In addition, the suitability assessment of land for construction can play an important role in spatial conflict analysis.
External traffic hub has a major impact on inner-city traffic, hence it is of great significance to study its passengers' OD distribution within the city for designing ground connection lines and stops along the route. Based on the Taxi GPS data in Beijing, this paper selected three typical types of external traffic hubs (airport, train station, bus passenger station) and studied the passengers' OD temporal-spatial distribution characteristics of external traffic hubs in Beijing by using methods of standard deviational ellipse, kernel density estimation and statistical analysis. The results showed that: (1) passenger traffic in the airport and the train station is much larger than the bus passenger station. Beijing Capital International Airport and Beijing West Railway Station have the largest outbound passenger capacity, while Beijing Nanyuan Airport and Beijing North Railway Station have fewer outbound passengers. (2) airport passengers are mainly distributed within most parts of the 4th Ring Road region and some parts of the 5th Ring Road region. The passenger traffic in the airport covers all the time periods except a period between 1 am and 4 am. Passengers of the train station are mainly distributed within most parts of the 4th Ring Road region and their traffic time mainly occures between 6 am and 9 pm. Passengers of each bus passenger station are mainly distributed in the peripheral area of itself and their traffic time mainly occures between 6 am and 5 pm. (3) Beijing South Railway Station has a strong connection with Beijing West Railway station and Beijing Railway station, so it is worth opening special connection lines between Beijing South Railway Station and the other two stations. In addition, the distribution areas for passengers of Beijing Capital International Airport and Beijing Nanyuan Airport are in accordance with certain new airport bus lines opened recently, which demonstrated that passengers' OD analysis based on taxi GPS data may indeed provide a good basis for the decision-making departments to conduct proper transportation planning.
Nepal, located in the core region of the Hindu-Kush-Himalayan region, has complicated and diverse land cover classes. Meanwhile, its topography shows a typical transition pattern from plain to mountain, and then to plateau. Land cover mapping of Nepal not only has a great scientific and practical significance to its land resource management and eco-environmental protection, but also contributes greatly to the basic data collection in this area, which will support China’s international regional economic cooperation strategy of “the Belt and Road Initiative”. In this paper, the land cover product of Nepal in 2010 (hereinafter referred to as the NepalCover-2010) was produced from Landsat TM image data sources, based on the combined cartographic method of object-oriented methods and decision trees. The product contains 8 primary categories and 32 secondary categories. Besides, the accuracy validation of the NepalCover-2010 was performed using samples obtained from high-resolution Google Earth imagery. The quantitative structural characteristics of Nepal and the relationships between the spatial distribution of typical land cover classes and the topographical and meteorological elements were analyzed. The results demonstrated that: (1) the overall accuracies of the primary and secondary categories for the NepalCover-2010 are 94.83% and 87.17% respectively. The Kappa coefficients are 0.94 and 0.85 respectively. The NepalCover-2010 can reflect the spatial distribution characteristics of land covers in Nepal. The comparison between the NepalCover-2010 with similar land cover products indicated that the NepalCover-2010 has the best accuracy. (2) Forest is the major land cover class in Nepal, and it accounts for 41% of the total land area of Nepal. Cultivated land accounts for 25% and the area ratio of paddy field to dry land is about 2:3. (3) The topographical and meteorological elements impact greatly on the spatial distribution of land cover classes. The spatial distribution of each land cover class possesses the characteristics of vertical zonality, and the appearance pattern of the typical land cover classes from the south to the north is paddy fields, evergreen broadleaf forests, dry lands, evergreen broadleaf shrubs, evergreen needleleaf forests, steppes, sparse vegetation, snow and ice, along with the increase in altitude.
As a typical land-cover type, impervious surface is a key indicator of urban environmental quality and urbanization scope. In comparison with the traditional remote sensing image processing methods, the assessment of impervious surface percentage (ISP) can offer the sub-pixel level exploration and acquire the fine-scale information. In this paper, the proposed method uses the Cubist model tree with both the high-resolution (Google Earth) and the medium-resolution (Landsat TM/ETM+) remote sensing data to establish an estimation model of impervious surface percentage (ISP). A base model (Base Cubist-ISP) is built integrating all the original bands from Landsat TM excluding the thermal infrared band. This paper tries to minimize the effects of noise by adopting the ensemble learning algorithm and by incorporating the median of each solar-reflective band within the adjacent temporal images. After that, the following variables are filtered to get the optimized results, including the TM thermal infrared band, the derived variables from the original bands such as Texture, and the tasseled cap transformation variables. Then the variables are simplified, and in that way, the optimized parameter of ensemble learning algorithm for Cubist tree and the well-chosen variables are used to establish an optimization estimation model (Optimal Cubist-ISP). The results of a case study for Haizhu district, which is located in Guangzhou city of Guangdong Province, show that the overall root mean square error between the estimated ISP value, which is based on the Optimal Cubist-ISP model, and the reference ISP value is 12.98%, with a determinant coefficient of 0.90. Moreover, this paper compares the Base Cubist-ISP model with the Optimal Cubist-ISP model. The accuracy of the Optimal Cubist-ISP model is better than the Base Cubist-ISP model, and the RMSE decreases by about 5.03%. It is illustrated that the Base Cubist-ISP model may over-estimate the pervious surface area and under-estimate the high density impervious surface area, which could be improved by the model optimization. In addition, the Optimal Cubist-ISP model can not only be able to well recognize the land types of soil and water, but also eliminate the influence of shadow on the high density building area to a certain extent. Thus, the proposed approach on impervious surface estimation based on the Cubist model tree as well as its optimization scheme can be applied for precisely obtaining the ISP in the urban areas.
Remotely sensed time series are being widely used in land surface information detection. However, influenced by the sensors and external conditions, different levels of noises exist in the remotely sensed time series. Although reconstruction models can reduce the noises in times series effectively, different reconstruction models provide different levels of accuracy when they are used at various intervals. This study took the city of Chaoyang in Liaoning Province as a case. We utilized the time series of Normalized Difference Vegetation Index (NDVI) at intervals of 1-day, 4-day, 8-day, 16-day and 30-day, respectively, to carry out experiments of simulation and phenology observation. We also assessed the reconstruction results of the SG filter model, the DL fitting model and the HANTS model based on their capabilities of keeping waveform of time series and their accuracy of phenological date extraction. In addition, we also analyzed sensitivity of these three models to various intervals. The results showed that the SG filter model performed better at larger intervals, the DL fitting model gave better reconstruction accuracy at smaller intervals and the Hants model gave better accuracy when it is used at larger intervals. Moreover, the reasons of the different performance of the three reconstruction models were analyzed from the theories of these models. On this basis, we gave the suggestions on the choice of reconstruction models of time series at different intervals.
The city of Suzhou is located in the well known Suzhou-Wuxi-Changzhou subsidence zone in China, which suffers from serious land subsidence. Land subsidence occurring in this area can cause conspicuous social and economic lost, thus a proper investigation is necessary for this region. In order to understand the spatial-temporal evolution of land subsidence in Suzhou, we applied the Small Baseline Subset (SBAS) Interferometric Synthetic Aperture Radar (InSAR) technique to 27 C-band ERS-2 SAR images acquired between 2007 and 2010 by European Space Agency (ESA). The results show that during the observation period, the downtown and uptown areas show a relatively slow subsidence rate, while an exceptionally rapid subsidence is detected in 3 suburb areas. The downtown area such as Gusu district and the uptown area such as Wuzhong district are characterized by their generally low subsidence rates, which are less than 10 mm/a, and there is no subsidence center detected. Land subsidence mainly occurs in Xiangcheng district, Wujiang district and the industrial park, which are the newly developed zones characterized by the subsidence rates of greater than 10 mm/a. We observe the prevalence of ground subsidence phenomena ranging from 10 mm/a up to 20 mm/a in Xiangcheng district, while in certain towns and streets, the subsidence phenomenon is significantly severe with a rate close to 20 mm/a or higher. The maximum accumulative subsidence in the industrial park reaches 50 mm, and the average subsidence rate of this area is approximately 20 mm/a. Rapid subsidence with an average rate being up to 20 mm/a is observed in Wujiang district, which is located in the southern part of Suzhou. The land subsidence in this area is the severest within the whole research area, featured by its large coverage and high subsidence rate, and the maximum accumulative subsidence displacement can reach 60 mm or more.
The suspended solids content in ocean water is a significant water quality parameter, and the use of remote sensing technology to monitor the suspended sediment concentration (SSC) is an important research direction in ocean color remote sensing. In this paper, based on a set of in situ data sets, which include the simultaneous spectral reflectance data and the SSC data of different depths obtained from 35 field sites distributed within the four profiles of Caofeidian waters, and combined with the Landsat-5 TM remote sensing data, an empirical statistical model between the SSC levels of water surface and the remote sensing reflectance was established. So the SSC levels of water surface in the study area were estimated. Subsequently, by respectively analyzing the correlation relationships of SSC levels between the surface and the middle layers, as well as the surface and the bottom layers, we deduced the SSC inversion results of the middle and bottom layers based on the results of surface SSC levels and we also studied the vertical spatial distribution patterns of SSC. The study results indicate that there is an obvious correlation between the SSC levels in the vertical direction. Based on the reflectance ratio (RTM3/RTM2) of Landsat-5 TM image, an optimized quadratic polynomial model for the SSC retrieval of water surface was established. Based on this model, the relevant models for the middle and bottom layers were also established. Then, the inversion precision of each model was validated using seven check points. The model's mean relative error of each layer was controlled to be less than 30%, the mean absolute errors and root mean square errors of the surface and middle layers were below 6 mg/L and 10 mg/L respectively. The precision of the bottom layer was slightly lower than the surface and middle layers. The results provide a foundation to further study the sediment transport rule of marine environment and the optimization of sediment transport model under hydrodynamic forces.
