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.

Improved strategy for estimating stem volume and forest biomass using moderate resolution remote sensing data and GIS

This study presents the utility of remote sensing （RS）, GIS and field observation data to estimate above ground biomass （AGB） and stem volume over tropical forest environment. Application of those data for the modeling of forest properties is site specific and highly uncertain, thus further study is encouraged. In this study we used 1460 sampling plots collected in 16 transects measuring tree diameter （DBH） and other forest properties which were useful for the biomass assessment. The study was carded out in tropical forest region in East Kalimantan, Indo- nesia. The AGB density was estimated applying an existing DBH - biomass equation. The estimate was superimposed over the modified GIS map of the study area, and the biomass density of each land cover was calculated. The RS approach was performed using a subset of sample data to develop the AGB and stem volume linear equation models. Pearson correlation statistics test was conducted using ETM bands reflectance, vegetation indices, image transform layers, Principal Component Analysis （PCA） bands, Tasseled Cap （TC）, Grey Level Co-Occurrence Matrix （GLCM） texture features and DEM data as the predictors. Two linear models were generated from the significant RS data. To analyze total biomass and stem volume of each land cover, Landsat ETM images from 2000 and 2003 were preprocessed, classified using maximum likelihood method, and filtered with the majority analysis. We found 158±16 m^3.ha^-1 of stem volume and 168±15 t.ha^-1 of AGB estimated from RS approach, whereas the field measurement and GIS estimated 157±92 m^3.ha^-1 and 167±94 t.ha^-1 of stem volume and AGB, respectively. The dynamics of biomass abundance from 2000 to 2003 were assessed from multi temporal ETM data and we found a slightly declining trend of total biomass over these periods. Remote sensing approach estimated lower biomass abundance than did the GIS and field measurement data. The earlier approach predicted 10.5 Gt and 10.3 Gt of total biomasses in 2000 and 2003, while the later estimated 11.9 Gt and 11.6 Gt of total biomasses, respectively. We found that GLCM mean texture features showed markedly strong correlations with stem volume and biomass.

On Statistical Measures for Data Quality Evaluation

Most GIS databases contain data errors. The quality of the data sources such as traditional paper maps or more recent remote sensing data determines spatial data quality. In the past several decades, different statistical measures have been developed to evaluate data quality for different types of data, such as nominal categorical data, ordinal categorical data and numerical data. Although these methods were originally proposed for medical research or psychological research, they have been widely used to evaluate spatial data quality. In this paper, we first review statistical methods for evaluating data quality, discuss under what conditions we should use them and how to interpret the results, followed by a brief discussion of statistical software and packages that can be used to compute these data quality measures.

Hypergraph-based Object-oriented Model and Hypergraph Theory for GIS

This paper discusses the features and relevant theories of GIS spatial data model based on hypergraph,etc.The integrated concept model based on hypergraph and object_oriented model (HOOM) is proposed by the authors.The principal contribution of this paper is that we study the K_section and other theories of hypergraph.An application example using HOOM is given at the end of the paper.

A ubiquitous knowledgeable data representation model(UKRM) for three-dimensional geographic information systems(3D GIS)

In the face of complicated, diversified three-dimensional world, the existing 3D GIS data models suffer from certain issues such as data incompatibility, insufficiency in data representation and representation types, among others. It is often hard to meet the requirements of multiple application purposes(users) related to GIS spatial data management and data query and analysis, especially in the case of massive spatial objects. In this study, according to the habits of human thinking and recognition, discrete expressions(such as discrete curved surface(DCS), and discrete body(DB)) were integrated and two novel representation types(including function structure and mapping structure) were put forward. A flexible and extensible ubiquitous knowledgeable data representation model(UKRM) was then constructed, in which structurally heterogeneous multiple expressions(including boundary representation(B-rep), constructive solid geometry(CSG), functional/parameter representation, etc.) were normalized. GIS's ability in representing the massive, complicated and diversified 3D world was thus greatly enhanced. In addition, data reuse was realized, and the bridge linking static GIS to dynamic GIS was built up. Primary experimental results illustrated that UKRM was overwhelmingly superior to the current data models(e.g. IFC, City GML) in describing both regular and irregular spatial objects.

Design of Service-Oriented Architecture for Spatial Data Integration and Its Application in Building Web-based GIS Systems

In this paper we propose a service-oriented architecture for spatial data integration (SOA-SDI) in the context of a large number of available spatial data sources that are physically sitting at different places, and develop web-based GIS systems based on SOA-SDI, allowing client applications to pull in, analyze and present spatial data from those available spatial data sources. The proposed architecture logically includes 4 layers or components; they are layer of multiple data provider services, layer of data in-tegration, layer of backend services, and front-end graphical user interface (GUI) for spatial data presentation. On the basis of the 4-layered SOA-SDI framework, WebGIS applications can be quickly deployed, which proves that SOA-SDI has the potential to reduce the input of software development and shorten the development period.

Temporal-spatial risk assessment of COVID-19 under the influence of urban spatial environmental parameters:The case of Shenyang city

Respiratory infection is the main route for the transmission of coronavirus pneumonia,and the results have shown that the urban spatial environment significantly influences the risk of infection.Based on the Wells-Riley model of respiratory infection probability,the study determined the human respiratory-related parameters and the effective influence range;extracted urban morphological parameters,assessed the ventilation effects of different spatial environments,and,combined with population flow monitoring data,constructed a method for assessing the risk of Covid-19 respiratory infection in urban-scale grid cells.In the empirical study in Shenyang city,a severe cold region,urban morphological parameters,population size,background wind speed,and individual behavior patterns were used to calculate the distribution characteristics of temporal and spatial concomitant risks in urban areas grids under different scenarios.The results showed that the correlation between the risk of respiratory infection in urban public spaces and the above variables was significant.The exposure time had the greatest degree of influence on the probability of respiratory infection risk among the variables.At the same time,the change in human body spacing beyond 1 m had a minor influence on the risk of infection.Among the urban morphological parameters,building height had the highest correlation with the risk of infection,while building density had the lowest correlation.The actual point distribution of the epidemic in Shenyang from March to April 2022 was used to verify the evaluation results.The overlap rate between medium or higher risk areas and actual cases was 78.55%.The planning strategies for epidemic prevention and control were proposed for the spatial differentiation characteristics of different risk elements.The research results can accurately classify the risk level of urban space and provide a scientific basis for the planning response of epidemic prevention and control and the safety of public activities.