Detecting and Visualizing Observation Hot-Spots in Massive Volunteer-Contributed Geographic Data across Spatial Scales Using GPU-Accelerated Kernel Density Estimation

Volunteer-contributed geographic data (VGI) is an important source of geospatial big data that support research and applications. A major concern on VGI data quality is that the underlying observation processes are inherently biased. Detecting observation hot-spots thus helps better understand the bias. Enabled by the parallel kernel density estimation (KDE) computational tool that can run on multiple GPUs (graphics processing units), this study conducted point pattern analyses on tens of millions of iNaturalist observations to detect and visualize volunteers’ observation hot-spots across spatial scales. It was achieved by setting varying KDE bandwidths in accordance with the spatial scales at which hot-spots are to be detected. The succession of estimated density surfaces were then rendered at a sequence of map scales for visual detection of hot-spots. This study offers an effective geovisualization scheme for hierarchically detecting hot-spots in massive VGI datasets, which is useful for understanding the pattern-shaping drivers that operate at multiple spatial scales. This research exemplifies a computational tool that is supported by high-performance computing and capable of efficiently detecting and visualizing multi-scale hot-spots in geospatial big data and contributes to expanding the toolbox for geospatial big data analytics.

THE ROLE OF GEOMATICS ENGINEER IN SMART CITIES: A VIEW WITHIN THE FRAMEWORK OF TURKISH 2020–2023 NATIONAL SMART CITIES STRATEGY AND ACTION PLAN

The development of technology resulted major revolutions in the cities. With the integration of technological developments into cities, the concept of smart cities began to emerge. Today, applications are made on smart cities in many countries. It is not possible to build a smart city without geographic data. It is one of the main duties of Geomatics Engineers to produce, use, process and finalize the geographic data and present it to the user. In this study, referring to the role of Geomatics Engineer in smart cities across Turkey 2020-2023 National Smart Cities Strategy and Action Plan framework is made in the investigations. When this plan is examined, it is seen that the importance of geographical/geo-spatial data and geo-information technologies for the realization of smart cities is an undeniable fact. In the 2020–2023 National Smart Cities Strategy and Action Plan, it has been clearly demonstrated that Geographic Information Systems and Geographic Information Technologies have a great role in creating smart cities.

PRODUCING AND VISUALIZATING 3D BUILDING GEODATABASE AS A PART OF 3D CADASTRE PROJECT

Digital data production possibilities have developed with the emerging technologies, and it has become possible to use different data formats together. The usability of three-dimensional (3D) data on various application areas has increased with the multidimensional use of geographic data in established information systems, for 3D visualization, presentation, and analysis. Topography-related analyzes such as digital elevation models, digital terrain models, slope maps and visibility maps can be made from geographic data sets produced in 3D. In addition, the use of 3D data in Building Information Modeling (BIM) has added various innovations for geographic data analysis. In this study, a geographic database was established by taking the vector data produced in the 3D Cadastre project that was carried out by the General Directorate of Land Registry and Cadastre as an example. Data obtained from photogrammetry and architectural projects were used in accordance with the OGC CityGML standard. After creating 3D building database in GIS environment, as result of various visualization and analysis techniques, the contributions of this project to BIM were revealed for various applications such as real estate valuation, disaster management, renewable energy, 3D city models, and smart city projects.

Interactive Web Maps: Usability Heuristics Proposal

With the evolution of technology, maps have changed how they are produced and consumed. In the 1990s, along with the internet uprise, printed and digital maps began to be shared and viewed on the web, which provided more significant user interaction with the map and geographic data. However, the ease of creating interactive maps using computational resources sometimes neglects cartographic concepts, impairing the interpretation of geographic data and the quality of the interaction between user and system. This work presents ten specific Usability Heuristics for Interactive Web Maps to identify and elaborate a set of criteria that help create and evaluate the quality of interactive web maps. For this, we used a methodology to develop domain-specific Usability Heuristics, composed of eight steps. This paper presents the ten heuristics elaborated along with the attributes of the name, ID, category and definition, and an additional checklist. This new set encompasses both the concepts of cartography and usability, contributing to better user interaction with the system and geographic data.

Comparison of tree distribution in the province of Baghdad and other random by using high-resolution satellite image

In this paper two, sites (Satellite„ Images) of planting groups (green areas) gathered from two different region in locale of Baghdad province : the first region represent luxury area and the other represent poor random region will„ be considered to recognize between„ them. The first group is made up of the most important plans in the province of„ Baghdad, while the second is a random gathering. The employing data might be a high-„ resolution adherent image, and the extricated scenes of a high-„ resolution toady image will„ be evaluated to be„ able to distinguish between them specifically by obsequious photos. The outcomes will be obtained using the„ Geographic data framework (GIS„Version 9.2) application.

Urban Functional Zone Recognition Integrating Multisource Geographic Data

As the basic spatial unit of urban planning and management, it is necessary to understand the real development trend of urban functional zones in time and carry out reasonable planning adjustment. Because of the complexity of urban functional zones, the automatic recognition of urban functional zones has become a significant scientific problem in urban research. Urban functional zones contain natural and socioeconomic characteristics, but the existing identification methods fail to comprehensively consider these features. This paper proposes a framework that integrates multisource geographic data to recognize urban functional zone. We used high-resolution remote sensing imagery, point-of-interest (POI) data and high-spatial-resolution nighttime light imagery to extract both natural and socioeconomic features for urban functional zone accurate interpretation. Various features provide more accurate and comprehensive description for complex urban functional zone, so as to improve the recognition accuracy of urban functional zone. At present, there are few studies on urban functional zone recognition based on the combination of high-resolution remote sensing image, POI and high-resolution nighttime light imagery. The application potential of the combination of these three geographical data sources in urban function zone recognition needs to be explored. The experimental results show that the accuracy of urban functional zone recognition was obviously improved by the three data sources combination, the overall accuracy reached 80.30% and a comprehensive evaluation index reached 68.26%. This illustrate that the combination of the three data sources is beneficial to the urban functional zone recognition.