Large-scale spatial data visualization method based on augmented reality

Background A task assigned to space exploration satellites involves detecting the physical environment within a certain space.However,space detection data are complex and abstract.These data are not conducive for researchers'visual perceptions of the evolution and interaction of events in the space environment.Methods A time-series dynamic data sampling method for large-scale space was proposed for sample detection data in space and time,and the corresponding relationships between data location features and other attribute features were established.A tone-mapping method based on statistical histogram equalization was proposed and applied to the final attribute feature data.The visualization process is optimized for rendering by merging materials,reducing the number of patches,and performing other operations.Results The results of sampling,feature extraction,and uniform visualization of the detection data of complex types,long duration spans,and uneven spatial distributions were obtained.The real-time visualization of large-scale spatial structures using augmented reality devices,particularly low-performance devices,was also investigated.Conclusions The proposed visualization system can reconstruct the three-dimensional structure of a large-scale space,express the structure and changes in the spatial environment using augmented reality,and assist in intuitively discovering spatial environmental events and evolutionary rules.

GPS probe map matching algorithm based on spatial data model

To improve the performance of the traditional map matching algorithms in freeway traffic state monitoring systems using the low logging frequency GPS （global positioning system） probe data, a map matching algorithm based on the Oracle spatial data model is proposed. The algorithm uses the Oracle road network data model to analyze the spatial relationships between massive GPS positioning points and freeway networks, builds an N-shortest path algorithm to find reasonable candidate routes between GPS positioning points efficiently, and uses the fuzzy logic inference system to determine the final matched traveling route. According to the implementation with field data from Los Angeles, the computation speed of the algorithm is about 135 GPS positioning points per second and the accuracy is 98.9%. The results demonstrate the effectiveness and accuracy of the proposed algorithm for mapping massive GPS positioning data onto freeway networks with complex geometric characteristics.

Spatiotemporal evolution and influencing factors of urban resilience in the Yellow River Basin,China

The Yellow River Basin of China is a key region that contains myriad interactions between human activities and natural environment.Industrialization and urbanization promote social-economic development,but they also have generated a series of environmental and ecological issues in this basin.Previous researches have evaluated urban resilience at the national,regional,urban agglomeration,city,and prefecture levels,but not at the watershed level.To address this research gap and elevate the Yellow River Basin’s urban resilience level,we constructed an urban resilience evaluation index system from five dimensions:industrial resilience,social resilience,environmental resilience,technological resilience,and organizational resilience.The entropy weight method was used to comprehensively evaluate urban resilience in the Yellow River Basin.The exploratory spatial data analysis method was employed to study the spatiotemporal differences in urban resilience in the Yellow River Basin in 2010,2015,and 2020.Furthermore,the grey correlation analysis method was utilized to explore the influencing factors of these differences.The results of this study are as follows:(1)the overall level of urban resilience in the Yellow River Basin was relatively low but showed an increasing trend during 2010–2015,and significant spatial distribution differences were observed,with a higher resilience level in the eastern region and a low-medium resilience level in the western region;(2)the differences in urban resilience were noticeable,with industrial resilience and social resilience being relatively highly developed,whereas organizational resilience and environmental resilience were relatively weak;and(3)the correlation ranking of resilience influencing factors was as follows:science and technology level>administrative power>openness>market forces.This research can provide a basis for improving the resilience level of cities in the Yellow River Basin and contribute to the high-quality development of the region.

Spatial Heterogeneity Modeling Using Machine Learning Based on a Hybrid of Random Forest and Convolutional Neural Network (CNN)

Spatial heterogeneity refers to the variation or differences in characteristics or features across different locations or areas in space. Spatial data refers to information that explicitly or indirectly belongs to a particular geographic region or location, also known as geo-spatial data or geographic information. Focusing on spatial heterogeneity, we present a hybrid machine learning model combining two competitive algorithms: the Random Forest Regressor and CNN. The model is fine-tuned using cross validation for hyper-parameter adjustment and performance evaluation, ensuring robustness and generalization. Our approach integrates Global Moran’s I for examining global autocorrelation, and local Moran’s I for assessing local spatial autocorrelation in the residuals. To validate our approach, we implemented the hybrid model on a real-world dataset and compared its performance with that of the traditional machine learning models. Results indicate superior performance with an R-squared of 0.90, outperforming RF 0.84 and CNN 0.74. This study contributed to a detailed understanding of spatial variations in data considering the geographical information (Longitude & Latitude) present in the dataset. Our results, also assessed using the Root Mean Squared Error (RMSE), indicated that the hybrid yielded lower errors, showing a deviation of 53.65% from the RF model and 63.24% from the CNN model. Additionally, the global Moran’s I index was observed to be 0.10. This study underscores that the hybrid was able to predict correctly the house prices both in clusters and in dispersed areas.

An Improved Hilbert Curve for Parallel Spatial Data Partitioning

A novel Hilbert-curve is introduced for parallel spatial data partitioning, with consideration of the huge-amount property of spatial information and the variable-length characteristic of vector data items. Based on the improved Hilbert curve, the algorithm can be designed to achieve almost-uniform spatial data partitioning among multiple disks in parallel spatial databases. Thus, the phenomenon of data imbalance can be significantly avoided and search and query efficiency can be enhanced.

Fast Web - Based Data Transmission

Since web based GIS processes large size spatial geographic information on internet, we should try to improve the efficiency of spatial data query processing and transmission. This paper presents two efficient methods for this purpose: division transmission and progressive transmission methods. In division transmission method, a map can be divided into several parts, called “tiles”, and only tiles can be transmitted at the request of a client. In progressive transmission method, a map can be split into several phase views based on the significance of vertices, and a server produces a target object and then transmits it progressively when this spatial object is requested from a client. In order to achieve these methods, the algorithms, “tile division”, “priority order estimation” and the strategies for data transmission are proposed in this paper, respectively. Compared with such traditional methods as “map total transmission” and “layer transmission”, the web based GIS data transmission, proposed in this paper, is advantageous in the increase of the data transmission efficiency by a great margin.

Fuzzy Clustering Validity for Spatia Data

The validity measurement of fuzzy clustering is a key problem. If clustering is formed, it needs a kind of machine to verify its validity. To make mining more accountable, comprehensible and with a usable spatial pattern, it is necessary to first detect whether the data set has a clustered structure or not before clustering. This paper discusses a detection method for clustered patterns and a fuzzy clustering algorithm, and studies the validity function of the result produced by fuzzy clustering based on two aspects, which reflect the un-certainty of classification during fuzzy partition and spatial location features of spatial data, and proposes a new validity function of fuzzy clustering for spatial data. The experimental result indicates that the new validity function can accurately measure the validity of the results of fuzzy clustering. Especially, for the result of fuzzy clustering of spatial data, it is robust and its classification result is better when compared to other indices.

Modeling data quality for risk assessment of GIS

This paper presents a methodology to determine three data quality (DQ) risk characteristics: accuracy, comprehensiveness and nonmembership. The methodology provides a set of quantitative models to confirm the information quality risks for the database of the geographical information system (GIS). Four quantitative measures are introduced to examine how the quality risks of source information affect the quality of information outputs produced using the relational algebra operations Selection, Projection, and Cubic Product. It can be used to determine how quality risks associated with diverse data sources affect the derived data. The GIS is the prime source of information on the location of cables, and detection time strongly depends on whether maps indicate the presence of cables in the construction business. Poor data quality in the GIS can contribute to increased risk or higher risk avoidance costs. A case study provides a numerical example of the calculation of the trade-offs between risk and detection costs and provides an example of the calculation of the costs of data quality. We conclude that the model contributes valuable new insight.

Relationship of Uncertainty Between Polygon Segment and Line Segment for Spatial Data in GIS

The mathematic theory for uncertainty model of line segment are summed up to achieve a general conception, and the line error hand model of εσ is a basic uncertainty model that can depict the line accuracy and quality efficiently while the model of εm and error entropy can be regarded as the supplement of it. The error band model will reflect and describe the influence of line uncertainty on polygon uncertainty. Therefore, the statistical characteristic of the line error is studied deeply by analyzing the probability that the line error falls into a certain range. Moreover, the theory accordance is achieved in the selecting the error buffer for line feature and the error indicator. The relationship of the accuracy of area for a polygon with the error loop for a polygon boundary is deduced and computed.

Research on methods of designing and building digital seabed database

With a review of the recent development in digitalization and application of seabed data, this paper systematically proposed methods for integrating seabed data by analyzing its feature based on ORACLE database management system and advanced techniques of spatial data management. We did research on storage structure of seabed data, distributed-integrated database system, standardized spatial database and seabed metadata management system in order to effectively manage and use these seabed information in practical application. Finally, we applied the methods researched and proposed in this paper to build the Bohai Sea engineering geology database that stores engineering geology data and other seabed information from the Bohai Sea area. As a result, the Bohai Sea engineering geology database can effectively integrate huge amount of distributed and complicated seabed data to meet the practical requisition of Bohai Sea en-gineering geology environment exploration and exploitation.

Spatial data mining system for ore-forming prediction

The authors designed the spatial data mining system for ore-forming prediction based on the theory and methods of data mining as well as the technique of spatial database,in combination with the characteristics of geological information data.The system consists of data management,data mining and knowledge discovery,knowledge representation.It can syncretize multi-source geosciences data effectively,such as geology,geochemistry,geophysics,RS.The system digitized geological information data as data layer files which consist of the two numerical values,to store these files in the system database.According to the combination of the characters of geological information,metallogenic prognosis was realized,as an example from some area in Heilongjiang Province.The prospect area of hydrothermal copper deposit was determined.

Effect of FDI on China's environmental pollution: Evidence based on spatial panel data

It is clearly stated in the 19th people's congress that we should make the environmental protection as our national policy. Therefore, it is of great importance to study this issue. This article is going to consider 30 provinces of China as the cross-section, and utilize the data sample from 2006 to 2015 of these cross-sections to formulate a Spatial Panel Data Durbin Model to analyze the effect of FDI. By using these data, this article creates a comprehensive environmental pollution index with the help of entropy. The result indicates that the effect of FDI on environment has a non-linear and spatial spillover characteristic. Before reaching the critical value, FDI has a negative effect on environment; however, with the accumulation of FDI, it will create a significant positive effect on the environment.

THE STRATEGIES OF INTEGRATING SPATI AL DATA ANALYSIS AND GIS

The gap between SDA(Spatial Data Analysis)and GIS(Geographical Information Systems )existed for a long time.Presently this problem still remains in spite of a lot of theore tical and practical studies which tr y to find the solu-tion for it.The research background and current situation about how to in tegrate SDA and GIS are introduced at first.The main idea of this article is to make su re what is the best scheme to bridge th e gap between SDA and GIS and how to design it.There are a lot of factors to influ ence the standards to assess such a sc heme,for instance,the attitude of users and GIS developers,the framework and related functions of current available GI S software in the market and so on.But the two most important ones of them are effic iency and flexibility of the scheme i tself.Efficiency can be measured by the conve-nient extent and temporal length when it is used for carrying out SDA.Flex ibility means users can define their own SDA methods.The best integration schem e should satisfy the two standards at the same time.A group of functions,which can be combined to implement any SDA meth od,are defined in order to design such an integration scheme.The functio ns are divided into five classes according to their properties.

DATABASE STRUCTURE FOR THE INTEGRATION OF RS WITH GIS BASED ON SEMANTIC NETWORK

The integration of remote sensing (RS) with geographical information system (GIS) is a hotspot in geographical information science.A good database structure is important to the integration of RS with GIS,which should be beneficial to the complete integration of RS with GIS,able to deal with the disagreement between the resolution of remote sensing images and the precision of GIS data,and also helpful to the knowledge discovery and exploitation.In this paper,the database structure storing the spatial data based on semantic network is presented.This database structure has several advantages.Firstly,the spatial data is stored as raster data with space index,so the image processing can be done directly on the GIS data that is stored hierarchically according to the distinguishing precision.Secondly,the simple objects are aggregated into complex ones.Thirdly,because we use the indexing tree to depict the relationship of aggregation and the indexing pictures expressed by 2_D strings to describe the topology structure of the objects,the concepts of surrounding and region are expressed clearly and the semantic content of the landscape can be illustrated well.All the factors that affect the recognition of the objects are depicted in the factor space,which provides a uniform mathematical frame for the fusion of the semantic and non_semantic information.Lastly,the object node,knowledge node and the indexing node are integrated into one node.This feature enhances the ability of system in knowledge expressing,intelligent inference and association.The application shows that this database structure can benefit the interpretation of remote sensing image with the information of GIS.

Evaluating Water Withdrawals for Regional Water Management Under a Data-driven Framework

With an increase in population and economic development,water withdrawals are close to or even exceed the amount of water available in many regions of the world.Modelling water withdrawals could help water planners improve the efficiency of water use,water resources allocation,and management in order to alleviate water crises.However,minimal information has been obtained on how water withdrawals have changed over space and time,especially on a regional or local scale.This research proposes a data-driven framework to help estimate county-level distribution of water withdrawals.Using this framework,spatial statistical methods are used to estimate water withdrawals for agricultural,industrial,and domestic purposes in the Huaihe River watershed in China for the period 1978–2018.Total water withdrawals were found to have more than doubled,from 292.55×10^(8)m^(3) in 1978 to 642.93×10^(8)m^(3) in 2009,and decreased to 602.63×10^(8)m^(3) in 2018.Agricultural water increased from 208.17×10^(8)m^(3) in 1978 to 435.80×10^(8)m^(3) in 2009 and decreased to 360.84×10^(8)m^(3) in 2018.Industrial and domestic water usage constantly increased throughout the 1978–2018 period.In 1978,industrial and domestic demands were 20.35×10^(8)m^(3) and 60.04×10^(8)m^(3),respectively,and up until 2018,the figures were 105.58×10^(8)m^(3) and 136.20×10^(8)m^(3).From a spatial distribution perspective,Moran’s I statistical results show that the total water withdrawal has significant spatial autocorrelation during 1978–2018.The overall trend was a gradual increase in 1978–2010 with withdrawal beginning to decline in 2010–2018.The results of Getis-Ord G_(i)^(*)statistical calculations showed spatially contiguous clusters of total water withdrawal in the Huaihe River watershed during1978–2010,and the spatial agglomeration weakened from 2010 to 2018.This study provides a data-driven framework for assessing water withdrawals to enable a deeper understanding of competing water use among economic sectors as well as water withdrawal modelled with proper data resource and method.

Diffusion of Spatial Data Infrastructures： Private More Inspired than the Public Sector---A Case of Tanzania

Spatial data is a key resource for national development. There is a lot of potential locked in spatial data and this potential may be realized by making spatial data readily available for various applications. SD1 （Spatial Data Infrastructures） provides a platform for the data users, producers and so on to generate and share spatial data effectively. Though efforts to develop spatial data infrastructures started worldwide in the late 1970s, SDIs are still perceived by many institutions as new innovation; as such, they have not penetrated to all institutions to bring about effective management and development changes. This paper is reporting on a study conducted to assess SDI Readiness Index for Tanzania. The study aimed at identifying problems undermining SD1 implementation in Tanzania, despite its potential in bringing fast socio-economic development elsewhere in the world. This paper is based on a research based on views from stakeholders of geospatial technology industry in Municipal Councils, Private Companies and Government Departments in Tanzania. Results indicated that Private Companies are more inspired than Government institutions towards implementation of SDIs. And those problems affecting implementation of SDIs are lack of National SDI Policy, lack of awareness and knowledge about SDIs, limited funding to operationalise SDI, lack of institutional leadership to coordinate SDI development activities, lack of political commitment from the Government. It is recommended that delibate efforts be devised to raise awareness of SDI amongst the Tanzanian community.

Object-Based Analysis of Multispectral RS Data and GIS for Detection of Climate Change Impact on the Karakoram Range Northern Pakistan

Changing climate has a great impact on northern area of Pakistan＇s environment and is more prone to environmental changes impacts than rest of the country due to its high elevation. However, melting glaciers effect not only the local environment but also the whole country with frequent and heavy floods. Remote sensing （RS） from Satellites and Airplanes used in Geographical Information Systems （GIS） are technologies that can aid in understanding the on-going environmental processes. Furthermore, help researchers to observe, understand, forecast and suggest response to changes that occur. It can be natural disasters or man-made disasters and human induced factors. Still analysis accuracy issues play a vital role for the formulation of any strategy. To achieve better results, object based analysis methods have been tested. Various algorithms are developed by the analysts to calculate the magnitude of land cover changes. However, they must be evaluated for each environment that is under observation as mountainous areas. Here were object-based methods evaluated in comparison with pixel based. Landslides, soil moisture, soil permeability, snow cover and vegetation cover can be effectively monitored by those methods.

Spatial Data Mining to Support Environmental Management and Decision Making--A Case Study in Brazil

The growth of geo-technologies and the development of methods for spatial data collection have resulted in large spatial data repositories that require techniques for spatial information extraction, in order to transform raw data into useful previously unknown information. However, due to the high complexity of spatial data mining, the need for spatial relationship comprehension and its characteristics, efforts have been directed towards improving algorithms in order to provide an increase of performance and quality of results. Likewise, several issues have been addressed to spatial data mining, including environmental management, which is the focus of this paper. The main original contribution of this work is the demonstration of spatial data mining using a novel algorithm with a multi-relational approach that was applied to a database related to water resource from a certain region of S^o Paulo State, Brazil, and the discussion about obtained results. Some characteristics involving the location of water resources and the profile of who is administering the water exploration were discovered and discussed.

Spatial data modeling for coalfield geological environment

Presented a study on the design and implementation of spatial data modelingand application in the spatial data organization and management of a coalfield geologicalenvironment database.Based on analysis of a number of existing data models and takinginto account the unique data structure and characteristic, methodology and key techniquesin the object-oriented spatial data modeling were proposed for the coalfield geological environment.The model building process was developed using object-oriented technologyand the Unified Modeling Language (UML) on the platform of ESRI geodatabase datamodels.A case study of spatial data modeling in UML was presented with successful implementationin the spatial database of the coalfield geological environment.The modelbuilding and implementation provided an effective way of representing the complexity andspecificity of coalfield geological environment spatial data and an integrated managementof spatial and property data.

Spatial Data Application on Health (COVID-19) in Health Region 6 of Thailand

Purpose: A geographic information system (GIS) in a combination with health data is very useful for disease monitoring, prevention and control. The new sciences of the combination of GIS and classical health data, namely spatial epidemiology. Methods: Using the secondary health database (including COVID-19 data);data source is the 43 standard data folders of Thailand health statistic collected by Ministry of Public Health Thailand, collected between 2013 and 2022. The data of the Heath Region 6 of Thailand was used as an example. Twodata sets health data with spatial data Combined to new database and developed dashboard to present the information via web base system. Results: The dash board provides new perspective of disease distribution view. For example, the map on our dashboard reports the density of COVID-19 cases in each area. Based on the records, the densest areas shown are the urban area in each province. It can also be used for resource distribution and access time to health center of the area. Conclusion: The project reports the use of GIS and public health data to develop a dashboard for monitoring health resources and disease distributions in the health region 6 of Thailand. The major limitation of spatial epidemiology is the lack of or incomplete raw data input in the system.