Geographic Information Data Research Articles

QPredict: Using low quality volunteered geospatial data to evaluate high quality authority data

High-quality, typically administrative, geospatial data should adhere to established measurement and representation practices and be protected from malicious attacks. However, this kind of geospatial data may only be infrequently updated due to its often prolonged production process compared to a data source of volunteered geographic information such as OpenStreetMap. Existing approaches typically try to quality-assure geospatial data by comparing it to another reference data set of perceived higher quality - often another administrative dataset facing a similar update cycle. In contrast, this article tries to determine whether actual changes present in volunteered geographic information data such as OpenStreetMap, which also need to be applied in an administrative dataset (i.e., consists of actual changes in the real world), can be identified automatically. To that end, we present QPredict, a machine learning approach observing changes in volunteered geospatial data such as OpenStreetMap to predict issues with a target (administrative) data set. The algorithm is trained by exploiting geospatial object characteristics, intrinsic and extrinsic quality metrics and their respective changes over time. We evaluate the effectiveness of our approach on two data sets representing two mid-size cities in Germany and discuss our findings in terms of their applicability in use cases.

A novel decomposition-reconstruction-prediction hybrid model to predict precipitable water vapor from GNSS Observations

Abstract Precipitable water vapor (PWV) is one of the key factors in weather disaster preparedness and water forecasting. Due to the nonlinearity and nonstationarity of the PWV sequence, the existing models cannot achieve stable prediction accuracy. Therefore, this paper proposes a novel decomposition-reconstruction-prediction hybrid prediction model, named ICEEMDAN-PE-ARIMAX-1D CNN-BiLSTM, for predicting PWV. Firstly, the PWV sequence derived from the Global Navigation Satellite System (GNSS) is decomposed using the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). Secondly, the permutation entropy (PE) of each decomposed modal is calculated and the PWV sequence is reconstructed into low- and high-frequency components. Then, considering spatio-temporal information, geographic information and meteorological data (longitude, latitude, altitude, Doy, Hod, surface air pressure and temperature) aided modelling, the low- and high-frequency components are predicted using autoregressive integrated moving average with exogenous inputs (ARIMAX) and one-dimensional convolution neural network-bidirectional long-short-term memory (1D CNN-BiLSTM), respectively, and superimposed to obtain the predicted value and prediction intervals. Finally, the proposed model is validated for performance using the PWV derived from six GNSS stations. Compared with other models, the results show that ICEEMDAN-PE-ARIMAX-1D CNN-BiLSTM significantly improves PWV prediction performance, the mean RMSEs for the six stations are 0.3765 mm, 0.7517 mm, and 1.4696 mm for the 1-hour, 12-hour, and 24-hour forecasts, respectively.

Leveraging geographic information systems and data analytics for enhanced public sector decision-making and urban planning

Geographic Information Systems (GIS) and data analytics are revolutionizing public sector governance and urban planning by providing innovative tools for data-driven decision-making. This review explores the integration of GIS and data analytics as a means to address complex challenges in resource management, crisis response, environmental sustainability, and urban development. GIS enables spatial data collection, visualization, and analysis, while advanced data analytics, including predictive models and machine learning, enriches decision-making processes by uncovering actionable insights from diverse datasets. Applications in public governance include optimizing resource allocation, enhancing emergency response strategies, and managing environmental impacts. In urban planning, GIS facilitates land use management, infrastructure development, and the creation of smart cities powered by the Internet of Things (IoT). The review highlights successful case studies, showcasing how cities worldwide have leveraged these technologies to achieve sustainable development and improve the quality of life for their residents. Despite their potential, the implementation of GIS and data analytics faces challenges such as data quality issues, financial and technical constraints, privacy concerns, and resistance to change within bureaucratic systems. This review discusses these barriers and offers insights into overcoming them through advancements in technology, collaborative governance models, and increased stakeholder engagement. This concludes by emphasizing the transformative role of GIS and data analytics in fostering more transparent, efficient, and sustainable urban governance. By embracing these tools, public sector entities can better anticipate and address societal needs, paving the way for more resilient and inclusive communities. Future opportunities include leveraging artificial intelligence, expanding GIS use cases, and fostering international collaboration to further enhance public sector and urban planning outcomes.

Design of Travel Route Planning Model Based on Deep Learning

Tourism route planning is affected by the factors such as tourism destination and tourists’ preference, which leads to poor automatic matching of routes. A mathematical model of tourism route planning based on deep learning is proposed. Under the condition of total route constraints, according to the historical tourism preference information and prior information of tourists, a big data model for the distribution of personalized characteristics of tourists is established, spatial constraints and time constraints parameters are input, and the feature matching of geographic location information and tourist interest parameter information is carried out by using the deep learning method, and the statistical feature quantity of the parameters of the tourism route planning model is extracted. Under the control of the deep learning and geographic information data set, Carry out multi-constraint and multi-objective hierarchical analysis of tourists and tourism destinations, and realize the optimization design of tourism route planning algorithm. The simulation results show that the accuracy of this method is high and the deviation is small. It improves the satisfaction level of tourists and helps users complete a series of visual analysis tasks such as route mining, route planning and destination analysis.

Combining geographic information and climate data to develop urban building energy prediction models in Taichung, Taiwan

Climate change in Taiwan has extended and intensified the summer season, leading to a notable surge in energy demand for cooling systems, especially in densely populated regions. Building energy usage is directly correlated with cooling degree hours (CDHs), representing the hourly temperature differential between indoors and outdoors. This study employed high-resolution Taiwan ReAnalysis Downscaling (TReAD) data to develop an urban energy prediction model focusing on localized cooling demand in central Taiwan's urban areas. Validated against actual electricity consumption data, the model achieved an R2 value of 0.76. The study reveals that urban areas exhibit a high cooling demand during the hot season, exceeding 25,000 °C-h and with an annual energy consumption of 44–64 kWh/m2. Conversely, rural areas have a lower cooling demand – that is, below 8,000 °C-h, with an annual energy consumption of <10 kWh/m2.Considering the IPCC's RCP8.5 warming scenario, October shows a 20–40 % increase in cooling demand compared to July and May. This underscores the need to address rising energy consumption especially during the early and late stages of the hot season in response to climate change.

Healthy urban blue space design: Exploring the associations of blue space quality with recreational running and cycling using crowdsourced data

Urban blue space offers substantial health benefits by encouraging population physical activity. Despite much evidence on the nature-health nexus, the relationship between blue space and recreational exercises remains under-studied, limiting the realisation of health benefits in blue space design. Using crowdsourced data, including volunteered geographic information and street view image data, this study investigates the associations of blue space quality with recreational running and cycling in Rotterdam, the Netherlands. Results show that recreational exercise levels on street segments vary based on the blue space type and design. Compared to inland canals and rivers, small-scale recreational waterbodies are more conducive to running but not cycling, while both activities tend to cluster around the Nieuwe Maas River. Interestingly, the Water View Index shows a general negative association with both activities after adjusting for the blue space type. Besides the waterbody characteristics, eye-level environmental factors, including higher Green View Index, lower building density, more diverse land use, greater connected street network and fewer traffic elements, are associated with more running and cycling exercises. Results for visual complexity and neighbourhood population composition are mixed depending on the exercise type. These findings are further translated into spatial design patterns for developing exercise-supportive and health-promoting blue spaces.

Research on New Spatial Data Infrastructure Supports the Circulation of Geographic Information Data Elements

Abstract. Data is an important factor of production in the digital economy era and an important support for building a new development pattern. As an important part of data resources, geographic information data is also an important new factor of production, which plays a unique role in the construction of digital China and economic and social development. From the perspective of releasing the value of data elements, data infrastructure is a new type of infrastructure that provides integrated data aggregation, processing,circulation, application, operation, and security services for society under the support of network, computing power and other facilities. It is an organic whole covering hardware, software, open source protocols, standards, and mechanism design. Spatial data infrastructure (SDI) is a collection of technologies, policies and systems that realize the access and access of spatial data. Through the construction of new spatial data infrastructure (NSDI), it supports the circulation of geographic information data elements,making it an important part of data infrastructure. Firstly, this paper gives the connotation of geographic information data elements circulation. Secondly, it proposed to construct the circulation system of geographic information data elements on the basis of enumerating and summarizing the spatial data infrastructure, in order to better support the construction of digital China and economic and social development. Thirdly, the basic requirements, technical indicators and overall architecture of NSDI are designed. Finally, the opportunities and challenges faced by the circulation of geographic information data elements were summarized, mainly focusing on the aspects of policy mechanisms and security systems. At the end of the paper is a summary and acknowledgments.

Examining urban design strategies in historic districts: a systematic literature review

The global community faces the challenge of balancing the conservation of historic districts with urbanisation demands. This systematic literature review explores innovative strategies for conserving and revitalising historic districts within urban design. An analysis of literature from the Scopus database highlights this as a prominent research area, supported by international reviews emphasising the critical role of urban design strategies. The findings indicate that urban designers can utilise modern geographic information technology, spatial analysis, data support, community engagement, and transdisciplinary knowledge to regenerate historic districts effectively. However, there is a notable lack of systematic, in-depth empirical research, with a predominance of conservative and repetitive descriptive case studies. Although these studies logically explore the significance of historic districts, there is an urgent need for innovative research on urban design methodologies. This study aims to identify research gaps through a comparative analysis of international findings, offering new perspectives and theoretical support for the sustainable development of historic districts.

Application of UAV airborne LiDAR technology in highway engineering construction

Abstract The airborne LiDAR technology of UAVs is an efficient and high-precision spatial information acquisition technology. This paper introduces the airborne LiDAR technology of UAV in detail and studies the application of airborne LiDAR technology of UAV in highway construction based on the sixth section of the Leibokahalo-Yongshan connection line of G4216 Yibin Xinshi to Panzhihua Section. Engineering application practice shows that this technology can solve the difficult problem of obtaining basic geographic information data, and verify the rationality of the scheme and the matching degree with the field environment. In addition, compared with traditional survey technology, the technology has obvious advantages in the construction of highways in complex terrain, which helps to improve the quality of survey and design.

Application of Rotor UAV LiDAR in Marine Beach Mapping

Abstract This paper based on the Jiangsu Provincial Offshore Underwater Geographic Information Data Acquisition Pilot (North) project, uses the Dajiang M600 Pro six rotor UAV equipped with the Haida Digital Cloud ARS-200 airborne LiDAR equipment to acquire topographic data of the marine mudflats, and then Terrasolid software is used to process the data to obtain the point cloud results and DEM results of the marine mudflats, and finally, GPS-RTK is used to collect the check points of the surveyed area to check the accuracy of the results. The results show that the precision of point cloud results is better than ± 10cm, which verifies the feasibility of rotary-wing UAV LiDAR technology applied to marine beach mapping, and provides a new technical scheme for marine beach mapping.

A Spatial Accessibility Study of Public Hospitals: A Multi-Mode Gravity-Based Two-Step Floating Catchment Area Method

The multi-modal two-step floating catchment area (MM-2SFCA) method is an extension of the two-step floating catchment area (2SFCA) method that incorporates the impact of different transportation modes, thereby facilitating more accurate calculations of the spatial accessibility of public facilities in urban areas. However, the MM-2SFCA method does not account for the impact of distance within the search radius on supply–demand capacities, and it assumes an idealized supply–demand relationship. This paper introduces the gravity model into the MM-2SFCA method, proposing a multi-modal gravity-based 2SFCA (MM-G2SFCA) method to better account for distance decay and supply–demand relationships. Furthermore, a standardized gravity model is proposed based on the traditional gravity model. This model imposes constraints on upper and lower limits for distance decay weights without compromising the fundamental curve characteristics of the gravity model, thereby avoiding extreme weight scenarios. The accessibility of public hospitals in Shenzhen is evaluated through the integration of basic geographic information data, resident travel data, and official statistical data. The findings demonstrate that the standardized gravity model effectively addresses the issue of excessively high local distance weights in the traditional gravity model, making it more suitable as a distance decay function. The MM-G2SFCA method improves the consideration of distance and supply–demand relationships, thereby facilitating a more rational distribution of accessibility on a global scale. This study discovers differences in the spatial allocation of public hospital resources across the Shenzhen’s districts. Accessibility within the metropolitan core is significantly higher than that outside the core. Additionally, there is a notable difference in the level of accessibility among the districts. Accessibility is found to be better in district centers and along the main traffic arteries.

Benefit evaluation of HVAC and HVDC for offshore wind power transmission system under the multidimensional index

With the development of offshore wind power transmission technology and the emergence of new dimensions in deep-sea wind power scenarios, some indexes in the existing evaluation system cannot meet the comprehensive benefit evaluation of multiple offshore wind power transmission schemes in new operating scenarios, making the identification of the optimal economic distance of different schemes vague. Therefore, this paper integrates multiple parameters into the total life cycle cost and levelized cost of energy indexes for traditional and new transmission schemes in deep-sea scenarios, considers power generation income, environmental and carbon index, and proposes a multidimensional evaluation method for offshore wind power transmission systems, and analyzes the sensitivity of the constructed indicators. Then, using actual geographic information data, the established parameter fusion multidimensional evaluation method is used to analyze the technical and economic ranges of four offshore transmission schemes. The results show that the optimal economic critical interval of active commutation type high voltage direct current transmission system (CSC) is 98 km, which is about 23 % higher than that of voltage source converter based high voltage direct current transmission system (VSC). Therefore, CSC is more suitable for deep offshore wind power transmission than VSC and has better comprehensive technical and economic performance.

广西专用饲料稻“中早39”气候品质评价与精细化区划

Using the staged sowing experiment data and rice quality data of the specialized forage rice "Zhongzao 39"(hereinafter referred to as "Z-39") conducted in Nanning, Guangxi, combined with climate and geographic information data, the meteorological factors that play a key role in the formation of rice quality for the specialized forage rice "Z-39" are screened and determined. Based on the actual planting of specialize forage rice in Guangxi and the critical disaster causing indicators of "Z-39" specialize forage rice, the climate quality zoning index of "Z-39" is determined. GIS fine grid spatial simulation analysis method is used for refined spatial simulation analysis on key meteorological factors. The zoning indicator method and path analysis method are fully used for refined level zoning of the climate quality of Guangxi specialized forage rice "Z-39". It includes three levels: optimal, high-quality, and suboptimal. Ultimately, a refined zoning map for the climate quality of "Z-39" is developed, and the results are in line with the actual situation of forage rice cultivation in Guangxi. The aim is to provide a scientific basis for the rational planning and sustainable development of the specialized forage rice "Z-39" in Guangxi.

Quantifying the Relationship between Slope Spectrum Information Entropy and the Slope Length and Slope Steepness Factor in Different Types of Water-Erosion Areas in China

Topography critically affects the occurrence of soil erosion, and computing slope spectrum information entropy (SSIE) allows for the convenient mirroring of the patterns of macroscopic topographic variation. However, whether SSIE can be effectively utilized for the quantitative assessment of soil erosion across various types of water-erosion areas and the specific methodology for its application remain unclear. This study focused on the quantitative relationship between SSIE, the slope length and slope steepness (LS) factor within various types of water-erosion areas across different spatial scales in China using multi-source geographic information data and technical tools such as remote sensing and geographic information systems. The results revealed (1) clear consistency in the spatial patterns of SSIE and the LS factor, which both displayed a distinct three-step distribution pattern from south to north. (2) The power model (Y = A·X^B) demonstrated a superior capacity to explaining the relationship between SSIE and the LS factors compared to the linear or exponential models, as evidenced by a higher coefficient of determination (R2). R2 values of different evaluation units (second-grade water-erosion area, third-grade water-erosion area, 30 km × 30 km grid, and 15 km × 15 km grid) were 0.88, 0.88, 0.81, and 0.79, respectively. (3) Despite a range of variances across various spatial scale evaluation units and different types of water-erosion areas, no significant disparities were evident within the power model. These findings offer a new topographic factor that can be incorporated into models designed for the expedited evaluation of soil erosion rates across water-erosion areas. Information about the proximity of the SSIE to the LS factor is valuable for enhancing the practical utilization of SSIE in the quantitative evaluation of soil erosion.

Characterizing collaborative mapping projects. A methodological framework for analyzing volunteered geographic information and spatial data infrastructure convergence

Characterizing collaborative mapping projects. A methodological framework for analyzing volunteered geographic information and spatial data infrastructure convergence

Transition from machine intelligence to knowledge intelligence: A multi-agent simulation approach to technology transfer

Abstract The traditional machine intelligence system lacks deep understanding and reasoning ability. This study took the automatic driving system in multi-agent as an example to bring higher-level intelligence and decision-making ability to automatic driving through knowledge intelligence. It obtained real-world geographic information data from OpenStreetMap, preprocessed the data, and built a virtual environment. The inception model was used to identify information in environmental images, and the knowledge information of traffic regulations, road signs, and traffic accidents was expressed to build a knowledge map. The knowledge related to automatic driving was integrated, and automatic driving training was carried out through the reward mechanism and the deep Q-network (DQN) model. About 13 kinds of traffic situations were set up in the virtual environment, and the traditional machine intelligence autonomous driving and knowledge fusion autonomous driving multi-agent were compared. The results show that the average number of accidents in 100,000 km of traditional machine intelligence autonomous driving and knowledge fusion autonomous driving multi-agents was 3 and 1.4, and the average number of violations in 100,000 km was 4.3 and 1.8, respectively. The average graphics processing unit (GPU) utilization rate of knowledge fusion autonomous driving in 13 virtual environments was 75.9%, and the average peak GPU utilization rate was 96.1%. Knowledge fusion of autonomous driving multi-agents can effectively improve the safety of autonomous driving and enable autonomous driving multi-agents to have a higher level of decision-making ability.

Climate change impacts on magnitude and frequency of urban floods under scenario and model uncertainties

Many studies have confirmed that climate change leads to frequent urban flooding, which can lead to significant socioeconomic repercussions. However, most existing studies have not evaluated the impacts of climate change on urban flood from both event-scale and annual-scale dimensions. In addition, there are only few studies that simultaneously consider scenario and model uncertainties of climate change, and combine flood risk assessment and uncertainty analysis results to provide practical suggestions for urban drainage system management. This study uses the statistical downscaling method to calculate the design rainfall under ten rainfall return periods of four climate models and three climate change scenarios in 2040s, 2060s, and 2080s in various prefecture-level cities in China. The four climate models are HadGEM2- ES, MPI-ESM-MR, NorESM1-M and FGOALS-g2 models and the three climate change scenarios are constructed by different representative concentration pathways (RCP), namely RCP2.6, RCP4.5 and RCP8.5. On this basis, relying on the generated drainage systems using geographical information and other data, event-scale and annual-scale precipitation are combined to calculate the change ratio of annual flood volume expectation in prefecture-level cities in each future year compared with the current situation. Furthermore, the study evaluates scenario and model uncertainties of climate change, and then comprehensively integrates the flood risk and its uncertainties to provides suggestions for urban drainage system management.

Research on Visualization of Digital Ground Model Based on Satellite Image

3D printing technology has the advantages of high precision, low cost and environmental protection, and can provide a new and reliable technology for visual expression. This paper takes Qinghai Lake as the research object, uses remote sensing technology as the means to make a digital ground model, uses 3DMax software to convert geographic information data into data that can be visualized and expressed, and finally uses 3D printing technology to make a visual 3D solid model of Qinghai Lake basin. The experiment shows that 3D printing technology is feasible in the production of digital terrain model, and has broad prospects for development.

Technical Framework and Preliminary Practices of Global Geographic Information Resource Construction

Abstract. High-precision and real-time global geographic information data are fundamental and strategic resources in various fields such as safeguarding global strategic interests, studying global environmental changes, and planning for sustainable development. However, due to challenges related to ground control and obtaining reference information, the development of global geographic information resources faces significant hurdles in terms of geometric positioning, information extraction, and data mining. This paper starts with the characteristics of domestically produced remote sensing images and proposes a comprehensive technical framework centered around "uncontrolled geometric positioning, intelligent interpretation of typical elements, mining of multi-source data from abroad, and intelligent hybrid collection and compilation of Digital Elevation Models (DEMs)." The paper elaborates on the key technical challenges that need to be overcome and their corresponding solutions. It also outlines the development of relevant data products and production technical specifications. Multiple production-oriented software tools were developed, leading to the creation of a variety of data products in multiple types and scales, including global 30-meter land cover data, DEM data, core vector data, and more.

Research on Dynamic Updating Technology of Digital Maps for Mobile Communications Based on Multi source Remote Sensing Images

Abstract. With the rapid development of 5G networks in China, mobile communication network simulation technology has gradually become a widely studied direction in the academic and application fields. This study focuses on three-dimensional digital geographic information data for mobile communication, including a large amount of geographic information data that significantly affects the propagation of mobile communication radio waves, such as terrain height and ground use types. These data play a crucial role in coverage prediction, interference analysis, and frequency planning in various telecommunications planning software. This article starts from the perspective of mobile communication network management, and combines typical large-scale terrain data production practice projects for mobile communication to deeply study the process and system function design of combining geographic information systems with multi-source remote sensing data in mobile communication network monitoring and management. On this basis, this study designed and implemented a dynamic updated terrain database for mobile communication. This database can update geographic information data in real-time, ensuring the timeliness and accuracy of the data. At the same time, by optimizing the database structure and query algorithms, the speed and efficiency of data processing have been improved, providing efficient and reliable solutions for coverage prediction, optimization analysis, and planning management of large-scale mobile communication networks.In summary, dynamic updating database of three-dimensional digital geographic information has important application value in mobile communication network simulation technology. Through in-depth research and application of this database, the performance and service quality of mobile communication networks can be effectively improved, laying a solid foundation for future communication technology development.