Component-based Access Design Method for Dynamic Spatial Database

In order to realize the optimal access of dynamic spatial database, a component-based optimal access method of dynamic spatial database is proposed. The statistical information distribution model for storing the characteristic data of association rules is constructed in the dynamic spatial database. The fuzzy information features are extracted by using the dynamic component fusion clustering analysis method. Combined with the distributed association feature quantity, the fusion scheduling is carried out to control the dynamic information clustering. Combined with fuzzy c-means clustering analysis method, dynamic attribute classification analysis is carried out. The dynamic component block matching model is used for update iterative optimization, and the optimal access to the dynamic spatial database is realized in the cluster center. Simulation results show that this method has strong adaptability to the optimal access of dynamic spatial database, and has high accuracy and good convergence for data information extraction in dynamic spatial database.

Application of GIS on building the spatial database for supporting drainage management in Ninh Kieu District

Ninh Kieu District is located in the center of Can Tho City, established under the Government’s Decree No. 05/2004/ND-CP. This is a modern and rich district, with a history of formation and development associated with the Mekong Delta. According to statistics in 2019, Ninh Kieu District has an average population of 280,792 people, with a population density of 9,605 people/km2 and an area of 29.23 km2. In the current period, the urbanization process in Ninh Kieu District takes place at a fast and strong pace. This has made it impossible for the urban drainage system to keep up with this change, thereby creating great difficulties for the drainage management here. In order to deal with these enormous challenges, the managers in this area need to incorporate new technologies into drainage management. This paper presents the main result of building a spatial database to support drainage management for Ninh Kieu District. This is considered as a fundamental source of data for application of GIS in urban drainage research and management in Ninh Kieu District.

IGiS FOR MANAGING CITIES SMARTLY – URBAN GEOINFORMATICS

A city is a geographic entity and should be efficiently analysed and optimised through the use of geo-spatial technology. The certification for a city to be ‘Smart’ is measured on the basis of the liveable index, adequacy of water supply, assured supply of electricity, proper sanitation and solid waste management, efficient urban mobility, public transport, affordable housing, robust information technology connectivity, transparent and good governance, safety and security of citizens, modernised health and education infrastructure and citizen participation which will lead to sustainable development. Smart Cities require a perfect balancing of modernisation of city infrastructure and leveraging technology. Smart cities require Geo-smart mapping and visualization capabilities with applications for protecting groundwater resources, locating schools and health centres, locating garbage dumps and toilets, designing bus routes. The indigenously developed integrated platform of GIS, Image Processing, Photogrammetry and CAD, called IGiS has been leveraged by Scanpoint Geomatics Limited, Ahmedabad (SGL), India for implementing the Enterprise GIS for 7 smart cities in India. A centralised geo spatial database with a standard data model compliant set of maps/layers has been created for each city. The spatial layers are derived from 30cm resolution satellite data. Point data (locational information) is generated using DGPS surveys. The city assets are geographically mapped at a scale of 1:2000 and organised in a spatial database. Inputs required for operations and maintenance of every utility/facility are geo tagged and stored in the database. Web & Mobile GIS applications & Citizen portal are developed using the indigenous platform. Integration with other e-governance applications and spatial layer requirements of the Integrated Command and Control Centre are supported through RestAPI & OGC compliant web services. SGL’s Mobile GIS framework named Qpad comes handy for spatial data verification. IoT devices are used to gain insights for real-time handling of critical situations or emergencies. Having laid the foundation for driving smart cities in terms of the spatial database at a scale of 1:2000, the stage is set to look forward to the results. Plugging revenue leakages, better traffic management, information at a click during peak of the Corona pandemic, effective usage of open spaces and barren areas, planning the utility requirements by the corporation to accommodate for the urban explosion is the kind of harvest that is anticipated with abated breath. This paper demonstrates the suitability and capability of the indigenously developed common platform for image processing and GIS (IGiS Enterprise Suite) in building Smart City Applications and quantifying the results.

An Optimized K-means with Density and Distance-Based Clustering Algorithm for Multidimensional Spatial Databases

From massive and complex spatial database, the useful information and knowledge are extracted using spatial data mining. To analyze the complexity, efficient clustering algorithm for spatial database has been used in this area of research. The geographic areas containing spatial points are discovered using clustering methods in many applications. With spatial attributes, the spatial clustering problem have been designed using many approaches, but nonoverlapping constraints are not considered. Most existing data mining algorithms suffer in high dimensions. With nonoverlapping named as Non Overlapping Constraint based Optimized K-Means with Density and Distance-based Clustering (NOC-OKMDDC),a multidimensional optimization clustering is designed to solve this problem by the proposed system and the clusters with diverse shapes and densities in spatial databases are fast found. Proposed method consists of three main phases. Using weighted convolutional Neural Networks(Weighted CNN), attributes are reduced from the multidimensional dataset in this first phase. A partition-based algorithm (K-means) used by Optimized KMeans with Density and Distance-based Clustering (OKMDD) and several relatively small spherical or ball-shaped sub clusters are made by Clustering the dataset in this second phase. The optimal sub cluster count is performed with the help of Adaptive Adjustment Factor based Glowworm Swarm Optimization algorithm (AAFGSO). Then the proposed system designed an Enhanced Penalized Spatial Distance (EPSD) Measure to satisfy the non-overlapping condition. According to the spatial attribute values, the spatial distance between two points are well adjusted to achieving the EPSD. In third phase, to merge sub clusters the proposed system utilizes the Density based clustering with relative distance scheme. In terms of adjusted rand index, rand index, mirkins index and huberts index, better performance is achieved by proposed system when compared to the existing system which is shown by experimental result.

Regional Clusters, Similarities, and Changes in Turkey’s Wood Production

This study aimed to separate the wood production in regions and provinces of Turkey into homogeneous groups based on similarities by using the country’s wood production figures for 2013 and 2018. Within this context, the hierarchical Ward’s and non-hierarchical K-means clustering methods were used comparatively. Clustering analyses of 2 to 6 in number were performed via both methods, and the same regions mostly fell into the same cluster groups, although in different cluster combinations. The results showed that some provinces with rich forest areas did not produce enough wood. It was observed that these provinces were in the same clusters with provinces having a low amount of forest areas and low wood production. Over the five-year period, very few provinces and regions differed in line with the previous development plans. The creation of a spatial database for wood raw material production using the findings obtained in this study will contribute to the development of operational inventory methods that can be included in long- and medium-term forestry plans.

QualiOSM: An Architecture to Improve Data Completeness on OpenStreetMap

OpenStreetMap (OSM) is a large spatial database in which geographic information is voluntarily contributed by thousands of users. In Geographic Information Systems (GIS), and more specifically, in Volunteered Geographic Information (VGI), as in the case of OSM, the issue of data completeness is a constant concern, since users without technical knowledge actively participate in the processes of including, editing and excluding data. Also in thecase of OSM, users can add information to the objects assigning special labels for them. These labels are popularly called tags, and the process of assigning them to objects contributes to improving the attribute completeness, an important metric of data quality. In this context, this article proposes the QualiOSM architecture, which generates an automatic tag adder with the purpose of improving the completeness of address information for OSM objects in Brazil, using the reverse geocoding tools Nominatim, CEP Aberto and the database from Correios. The QualiOSM architecture showed good results for improving the completeness of city, neighborhood and street information in OSM objects, especially in scenarios of large urban centers, where the level of mapping is usually better compared to scenarios in rural or peripheral environments.

Spatial Database Designing for Environmental Monitoring and Decision Making in Mitrovica Region, The Republic of Kosovo

The integration of spatial data analysis methods and thematic map models is an approach to reduce the negative impact of anthropogenic pressure on the environment due to mining and waste generation. The large amounts of industrial waste from mining in the Mitrovica region in northern Kosovo lead to serious environmental problems with organic and inorganic water and soil pollution. This study aims to design and establish a geospatial database for long-term environmental monitoring, provide analytical tools, and support appropriate management decisions by local authorities and agencies. The database contains topographical elements and ecological parameters collected from different national and open access international sources. All collected data have been analyzed, standardized and harmonized within the open-source QGIS ver.3 software. The results showed that in developed datasets were organized in different GIS layers and compiled several thematic maps. The designed database is unique by its architecture, providing an opportunity for periodical monitoring of the environment near the mining areas. Keywords: Environmental monitoring; Spatial database; Open source software; QGIS; Kosovo. Copyright (c) 2021 Geosfera Indonesia and Department of Geography Education, University of Jember This work is licensed under a Creative Commons Attribution-Share A like 4.0 International License

INTEGRATION OF URBAN SPATIAL DATA MANAGEMENT AND VISUALIZATION WITH ENTERPRISE APPLICATIONS USING OPEN-SOURCE SOFTWARE

In recent years, efficient management of urban spatial data has played a major role in improving urban planning projects both in terms of cost and time savings. Since urban planning projects involve various disciplines like city planning and architecture as well as working with different spatial data, one of the main challenges is how to integrate and manage these multimodal data for a proper workflow. Currently, the involved companies are using project management and accounting systems, so called Enterprise Resource Planning (ERP) systems to handle these complex urban projects - that partly handle the same data objects as stored in urban spatial databases but without any spatial reference. Embedded in the application example of an urban redevelopment area, which according to the German Urban Development Promotion Act aims at financially promoting urban districts in need of renewal, project-related spatial and non-spatial data that were previously kept separate are linked and integrated. Therefore, our work presented here bridges the gap between these two types of application systems, the non-spatial accounting system called Finanz Management System (FMS) and the urban spatial databases. FMS manages information related to parcels, buildings, property owners, as well as the legally required payments connected to urban development, while an urban spatial database manages the geodata. We describe the prerequisites, procedures, and software development steps for coupling different types of applications by providing an example of the Enterprise Application Integration System (EAI). Our innovative integration process aims at making information from the spatial database available in FMS and vice versa, and allows updating the corresponding databases. Our work shows the potential of open-source software for cadastral data processing and visualization as well as accounting procedures for urban planning projects.