New roles of research data infrastructure in research paradigm evolution

Research data infrastructures form the cornerstone in both cyber and physical spaces,driving the progression of the data-intensive scientific research paradigm.This opinion paper presents an overview of global research data infrastructure,drawing insights from national roadmaps and strategic documents related to research data infrastructure.It emphasizes the pivotal role of research data infrastructures by delineating four new missions aimed at positioning them at the core of the current scientific research and communication ecosystem.The four new missions of research data infrastructures are:(1)as a pioneer,to transcend the disciplinary border and address complex,cutting-edge scientific and social challenges with problem-and data-oriented insights;(2)as an architect,to establish a digital,intelligent,flexible research and knowledge services environment;(3)as a platform,to foster the high-end academic communication;(4)as a coordinator,to balance scientific openness with ethics needs.

PARE:Privacy-Preserving Data Reliability Evaluation for Spatial Crowdsourcing in Internet of Things

The proliferation of intelligent,connected Internet of Things(IoT)devices facilitates data collection.However,task workers may be reluctant to participate in data collection due to privacy concerns,and task requesters may be concerned about the validity of the collected data.Hence,it is vital to evaluate the quality of the data collected by the task workers while protecting privacy in spatial crowdsourcing(SC)data collection tasks with IoT.To this end,this paper proposes a privacy-preserving data reliability evaluation for SC in IoT,named PARE.First,we design a data uploading format using blockchain and Paillier homomorphic cryptosystem,providing unchangeable and traceable data while overcoming privacy concerns.Secondly,based on the uploaded data,we propose a method to determine the approximate correct value region without knowing the exact value.Finally,we offer a data filtering mechanism based on the Paillier cryptosystem using this value region.The evaluation and analysis results show that PARE outperforms the existing solution in terms of performance and privacy protection.

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.

Analysis of the Relevance of Spatial Distribution Between Urban Parks and Transportation Infrastructure in Beijing Based on POI Data

In this paper,the relevance of spatial distribution between urban parks and transportation infrastructure in Beijing was studied grounded on POI data to guarantee the accuracy and scientificity.This paper focused on three aspects.Firstly,based on vector data of Beijing and POI data,the distribution of bus stations,subway stations,and parking lots as well as the road network density in the radiating region of each main entrance in urban parks were analyzed to assess traffic service ability in surrounding area of urban parks.Secondly,coupling relationship of spatial distribution between urban parks and transportation network in Beijing was analyzed using Kernel Density tools.Thirdly,Arc GIS was used to explore space pattern characteristics of large parks and association models between large parks and main transportation network,including two types of intersected and non-intersected and seven models.Finally,according to the comprehensive analysis of POI data,there was a contradictory relationship of interdependency and interaction between space pattern of urban parks and transportation infrastructure construction in Beijing,contributing to the scientific judgment of spatial characteristics of urban parks and transportation infrastructure as well as traffic accessibility and convenience.Quantified conclusions were thus obtained to explore coordinated development strategies for two systems of park green space and transportation network,thereby providing new ideas for the construction of harmonious and livable environment in Beijing.

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.

Towards a Spatial Data Infrastructure and an Integrated Management of Groundwater Resources

International studies of expertise have shown that the difficulty of data access is one of the major hindrances that brakes any effort to conduct studies on groundwater. This research paper is an attempt to address this problematic through developing a technical framework to implement a Spatial Data Infrastructure (SDI) with a view to improving the status of access to data related to groundwater in Morocco, and achieving their interoperability. This prototype is primarily based on international standards (OGC & ISO) such as Web Map Service (WMS), Web Feature Service (WFS), Catalog Web Service (CSW) and Sensor Observation Service (SOS) accompanied by the use of associated specifications such as Geography Markup Language (GML) and Filter Encoding (FE). This platform is considered both as a tool for sharing updated data collected from numerous and divers source providers, and as a tool of web-based GIS for groundwater management, which constitutes the basis for decision making.

A Diachronic Perspective on the Use of French Spatial Data Infrastructures

Despite the recent development of many worldwide initiatives, there is still a need for the development of observation frameworks that will provide a comprehensive view of SDI’s use. Amongst the many challenges left, a thorough analysis of the information flows between existing SDIs as well as their respective uses and the way that those evolve over time is an important issue to explore. The research presented in this paper introduces a methodological framework oriented to the study of the SDIs use from a diachronic perspective. The approach is based on a Social Network Analysis (SNA) and questionnaires collected by online surveys. We develop a structural and diachronic analysis based on a series of graph-based measures identifying the main patterns that appear over time. The methodological framework is applied to a series of French SDIs and users involved in environmental management. The study identifies a series of structural differences in the data flows that emerge between the users and SDIs. Last, the diachronic network analysis provides an overall understanding on how data flows evolve over time at different institutional levels.

Seismic data analysis based on spatial subsets

There are some limitations when we apply conventional methods to analyze the massive amounts of seismic data acquired with high-density spatial sampling since processors usually obtain the properties of raw data from common shot gathers or other datasets located at certain points or along lines. We propose a novel method in this paper to observe seismic data on time slices from spatial subsets. The composition of a spatial subset and the unique character of orthogonal or oblique subsets are described and pre-stack subsets are shown by 3D visualization. In seismic data processing, spatial subsets can be used for the following aspects： （1） to check the trace distribution uniformity and regularity; （2） to observe the main features of ground-roll and linear noise; （3） to find abnormal traces from slices of datasets; and （4） to QC the results of pre-stack noise attenuation. The field data application shows that seismic data analysis in spatial subsets is an effective method that may lead to a better discrimination among various wavefields and help us obtain more information.

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.

Spatial spillover effects of transport infrastructure on regional economic growth

Spatial spillover effects,either positive or negative,of transport infrastructure,highways/expressways,etc.,on regional economic growth are proposed.Using the panel data for 11 cities of Zhejiang province from 1994 to 2003,a spatial production function is applied to examine the spatial spillovers which can be generated as a positive output spillover from the transport infrastructure between neighboring cities.Some spatial weighted matrices are adopted to define different neighboring cities to measure how easily factors or economic activities can migrate between regions.The estimation results show that the output elasticity of the highway infrastructure in 11 cities are all insignificant at a 5% significance level;hence,highway infrastructure in a region cannot explain the same region's economic growth.On the other hand,the highway infrastructure of other contiguous regions has positive spillover effects on a same region's economic growth.

Scaling up the DBSCAN Algorithm for Clustering Large Spatial Databases Based on Sampling Technique

Clustering, in data mining, is a useful technique for discovering interesting data distributions and patterns in the underlying data, and has many application fields, such as statistical data analysis, pattern recognition, image processing, and etc. We combine sampling technique with DBSCAN algorithm to cluster large spatial databases, and two sampling based DBSCAN (SDBSCAN) algorithms are developed. One algorithm introduces sampling technique inside DBSCAN, and the other uses sampling procedure outside DBSCAN. Experimental results demonstrate that our algorithms are effective and efficient in clustering large scale spatial databases.

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.

Spatial Process of Green Infrastructure Changes Associated with Rapid Urbanization in Shenzhen,China

Through a case study of Shenzhen City,China,this study focused on a quantitative method for analyzing the spatial processes involved in green infrastructure changes associated with rapid urbanization.Based on RS,GIS and SPSS statistics software,the approach includes selection of the square analysis units and representative landscape metrics,quantification of the change types of landscape metrics in all analysis units through two indices and hierarchical cluster analysis of the above analysis units with different landscape metric change types(i.e.spatial attributes).The analyses verify that there is a significant sequence of continuous changes in green infrastructure in Shenzhen.They are the perforation,the segmentation,the fragmentation,the evanescence and the filling-in processes,which have a good spatio-temporal correspondence with urbanization and reflect the synthetic influence of urban planning,government policies and landforms.Compared with other studies on quantifying the spatial pattern,this study provides an alternative probe into linking the spatial pattern to spatial processes and the corresponding ecological processes in the future.These spatio-temporal processes offer many opportunities for identifying,protecting and restoring key elements in an urban green infrastructure network for areas in the early stages of urbanization or for non-urbanized areas.

Characteristics analysis on high density spatial sampling seismic data

China＇s continental deposition basins are characterized by complex geological structures and various reservoir lithologies. Therefore, high precision exploration methods are needed. High density spatial sampling is a new technology to increase the accuracy of seismic exploration. We briefly discuss point source and receiver technology, analyze the high density spatial sampling in situ method, introduce the symmetric sampling principles presented by Gijs J. O. Vermeer, and discuss high density spatial sampling technology from the point of view of wave field continuity. We emphasize the analysis of the high density spatial sampling characteristics, including the high density first break advantages for investigation of near surface structure, improving static correction precision, the use of dense receiver spacing at short offsets to increase the effective coverage at shallow depth, and the accuracy of reflection imaging. Coherent noise is not aliased and the noise analysis precision and suppression increases as a result. High density spatial sampling enhances wave field continuity and the accuracy of various mathematical transforms, which benefits wave field separation. Finally, we point out that the difficult part of high density spatial sampling technology is the data processing. More research needs to be done on the methods of analyzing and processing huge amounts of seismic data.

Improving Simulations of Vegetation Dynamics over the Tibetan Plateau:Role of Atmospheric Forcing Data and Spatial Resolution

The efficacy of vegetation dynamics simulations in offline land surface models(LSMs)largely depends on the quality and spatial resolution of meteorological forcing data.In this study,the Princeton Global Meteorological Forcing Data(PMFD)and the high spatial resolution and upscaled China Meteorological Forcing Data(CMFD)were used to drive the Simplified Simple Biosphere model version 4/Top-down Representation of Interactive Foliage and Flora Including Dynamics(SSiB4/TRIFFID)and investigate how meteorological forcing datasets with different spatial resolutions affect simulations over the Tibetan Plateau(TP),a region with complex topography and sparse observations.By comparing the monthly Leaf Area Index(LAI)and Gross Primary Production(GPP)against observations,we found that SSiB4/TRIFFID driven by upscaled CMFD improved the performance in simulating the spatial distributions of LAI and GPP over the TP,reducing RMSEs by 24.3%and 20.5%,respectively.The multi-year averaged GPP decreased from 364.68 gC m^(-2)yr^(-1)to 241.21 gC m^(-2)yr^(-1)with the percentage bias dropping from 50.2%to-1.7%.When using the high spatial resolution CMFD,the RMSEs of the spatial distributions of LAI and GPP simulations were further reduced by 7.5%and 9.5%,respectively.This study highlights the importance of more realistic and high-resolution forcing data in simulating vegetation growth and carbon exchange between the atmosphere and biosphere over the TP.

Unified Platform About Information Issuing,Integration and Management of Meta-Database and Spatial-Database Based on Geology Metadata Standard

With the deepening informationization of Resources & Environment Remote Sensing geological survey conducted,some potential problems and deficiency are:(1) shortage of unified-planed running environment;(2) inconsistent methods of data integration;and(3) disadvantages of different performing ways of data integration.This paper solves the above problems through overall planning and design,constructs unified running environment, consistent methods of data integration and system structure in order to advance the informationization

Spatial Characteristics of Ancient Villages in Western Beijing Based on ASTER GDEM Data

There are hundreds of villages in the western mountainous area of Beijing,of which quite a few have a profound history and form the settlement culture in the western part of Beijing.Taking dozens of ancient villages in Mentougou District as the research sample,the village space as the research object,based on ASTER GDEM database and quantitative analysis tools such as Global Mapper and ArcGIS,this study analyzed from the perspectives of altitude,topography,slope direction,and building density distribution,made a quantitative study on the spatial distribution and plane structure of ancient villages so that the law of village space with the characteristics of western Beijing was summarized to supplement and improve the relevant achievements in the research field of ancient villages in western Beijing.

DEVELOPMENT OF A GIS DATA MODEL WITH SPATIAL,TEMPORAL AND ATTRIBUTE COMPONENTS BASED ON OBJECT-ORIENTED APPROACH

This paper presents a conceptual data model, the STA-model, for handling spatial, temporal and attribute aspects of objects in GIS. The model is developed on the basis of object-oriented modeling approach. This model includes two major parts: (a) modeling the signal objects by STA-object elements, and (b) modeling relationships between STA-objects. As an example, the STA-model is applied for modeling land cover change data with spatial, temporal and attribute components.

A geographical similarity-based sampling method of non-fire point data for spatial prediction of forest fires

Understanding the mechanisms and risks of forest fires by building a spatial prediction model is an important means of controlling forest fires.Non-fire point data are important training data for constructing a model,and their quality significantly impacts the prediction performance of the model.However,non-fire point data obtained using existing sampling methods generally suffer from low representativeness.Therefore,this study proposes a non-fire point data sampling method based on geographical similarity to improve the quality of non-fire point samples.The method is based on the idea that the less similar the geographical environment between a sample point and an already occurred fire point,the greater the confidence in being a non-fire point sample.Yunnan Province,China,with a high frequency of forest fires,was used as the study area.We compared the prediction performance of traditional sampling methods and the proposed method using three commonly used forest fire risk prediction models:logistic regression(LR),support vector machine(SVM),and random forest(RF).The results show that the modeling and prediction accuracies of the forest fire prediction models established based on the proposed sampling method are significantly improved compared with those of the traditional sampling method.Specifically,in 2010,the modeling and prediction accuracies improved by 19.1%and 32.8%,respectively,and in 2020,they improved by 13.1%and 24.3%,respectively.Therefore,we believe that collecting non-fire point samples based on the principle of geographical similarity is an effective way to improve the quality of forest fire samples,and thus enhance the prediction of forest fire risk.

Spatial Quantitative Analysis of Garlic Price Data Based on ArcGIS Technology

In order to solve the hidden regional relationship among garlic prices,this paper carries out spatial quantitative analysis of garlic price data based on ArcGIS technology.The specific analysis process is to collect prices of garlic market from 2015 to 2017 in different regions of Shandong Province,using the Moran's Index to obtain monthly Moran indicators are positive,so as to analyze the overall positive relationship between garlic prices;then using the geostatistical analysis tool in ArcGIS to draw a spatial distribution Grid diagram,it was found that the price of garlic has a significant geographical agglomeration phenomenon and showed a multi-center distribution trend.The results showed that the agglomeration centers are Jining,Dongying,Qingdao,and Yantai.At the end of the article,according to the research results,constructive suggestions were made for the regulation of garlic price.Using Moran’s Index and geostatistical analysis tools to analyze the data of garlic price,which made up for the lack of position correlation in the traditional analysis methods and more intuitively and effectively reflected the trend of garlic price from low to high from west to east in Shandong Province and showed a pattern of circular distribution.