Application Of Multi-source Data Research Articles

Application of multi-source data in a tornado process in Jiangsu province

This paper analyzes the weather background and triggering mechanism before the occurrence of an EF3 tornado on 14 May 2021, in southern Jiangsu, using multi-source data. Results show that 1) the tornado occurred in the warm and humid area inside the low-level shear line and the inverted trough at the surface, the southeast wind field to the east of the surface convergence line and high value area of water vapor flux on the left side of low altitude jet axis exit zone. 2) The intensity of the heavy rainfall supercell storm was strengthened after entering the Taihu Lake, and mesocyclones were detected with decreasing heights and increasing shear strengths which leading to the formation of the tornado parent storm. Tornado vortex storm bottom height dropped to 0.3km, the shear value increased to 169.1 × 10−3 s-1, strong echoes above 60 dBZ reached to the ground, the difference between the lowest elevation Radial velocity (LLDV) and the maximum Radial velocity (MXDV) reached up to the largest value when tornado occurred. 3) The strong convergence of the ground and the enhancement of mesoscale frontal zone resulted in the strong development of thunderstorm cells. The deep rear inflow behind the heavy precipitation supercell storm passed through the stratiform cloud area and produced strong updraft due to the convergence of wind direction and wind speed. The updrafts generated vertical vorticity under the influence of wind shear, which was further stretched in the middle layer of the troposphere to form the core of the tornado. 4) The flat underlying surface, abundant water vapor, lake surface thermal boundary, and strong wind convergence on the east bank of Taihu Lake provided favorable conditions for the occurrence of the tornado. The centers of the mesocyclones in the lower and middle layers were close to each other in the horizontal direction, which provided stable and vertical updrafts for the mesocyclones and strengthened the suction effect of the mesocyclones on the low-level vortices, facilitating the formation of the tornado.

Evaluating the Spatial Quality of Urban Living Streets: A Case Study of Hengyang City in Central South China

Evaluating the spatial quality of a living street entails identifying and assessing the outdoor space that influences residents’ leisure and recreation, which may contribute to urban renewal. The application of multi-source data and deep learning technology enables an objective evaluation of large-scale spatial quality as opposed to the traditional questionnaire survey or experts’ subjective evaluation. Based on street view images, points of interest, and road network data, this study developed subjective and objective evaluation indicators for the central city of Hengyang using semantic segmentation and ArcGIS spatial analysis. This study then assigned weights to each indicator and calculated the spatial quality score for living streets. In addition, the subjective evaluations of the street view images were compared to test and verify the validation of the objective evaluation model. Finally, the study analyzed the accessibility within 500 m of the study area using Spatial Syntax and ArcGIS to overlay the low spatial quality score with the highest accessibility to identify the streets with the highest priority in the subsequent urban plan. The results indicate that the spatial quality of living in the west of Hengyang is higher than that in its northeast region. In addition, Xiao Xia Street, Guanghui Street, and Hengqi Road comprised the majority of the areas that required a priority update. Correspondingly, our research is expected to be a useful management tool for identifying urban street space issues and guiding urban renewal.

Equity Study on Urban Park Accessibility Based on Improved 2SFCA Method in Zhengzhou, China

The distribution of urban parks is closely related to the opportunities of daily use by residents as well as the performance of the park system. The question as to whether parks are distributed equitably within cities is therefore becoming the focus of attention. However, only a few studies have explored a comprehensive and systematic procedure for urban park accessibility analysis and equity evaluation. In this study, by applying an improved two-step floating catchment area (2SFCA) method and K-means cluster analysis, based on the application of multi-source data, we provide insights into an equity study on park accessibility at the neighborhood scale and urban ring scale in the central urban area of Zhengzhou. These results suggest that the spatial access to parks in Zhengzhou is generally unevenly distributed among neighborhoods, and both the mean and standard deviation of accessibility show an increase from the center to the periphery. The cluster analysis reveals a set of four types of neighborhoods, including a high-supply medium-demand medium-accessibility type (HMM), a low-supply medium-demand low-accessibility type (LML), a high-supply low-demand high-accessibility type (HLH), and a medium-supply high-demand low-accessibility type (MHL), each with different characteristics and causes. The spatial distribution of the accessibility types exhibits both similarities and differences between the urban rings. The findings of this study could serve as a tool for identifying areas in which parks are underserved and the ways in which they differ from other areas, which can guide urban planning to address specific inequities.

A Novel Tropical Cyclone Track Forecast Model Based on Attention Mechanism

Tropical cyclones are one of the most powerful and destructive weather systems on Earth. Accurately forecasting the landing time, location and moving paths of tropical cyclones are of great significance to mitigate the huge disasters it produces. However, with the continuous accumulation of meteorological monitoring data and the application of multi-source data, traditional tropical cyclone track forecasting methods face many challenges in forecasting accuracy. Recently, deep learning methods have proven capable of learning spatial and temporal features from massive datasets. In this paper, we propose a new spatiotemporal deep learning model for tropical cyclone track forecasting, which adopts spatial location and multiple meteorological factors to forecast the tracks of tropical cyclones. The model proposes a multi-layer ConvGRU to extract the nonlinear spatial features of tropical cyclones, while Spatial and Channel Attention Mechanism (CBAM) is adopted to overcome the large-scale problem of high response isobaric surface affecting the tropical cyclones. Meanwhile, this model utilizes a Deep and Cross framework to combine the traditional CNN model with the multi-ConvGRU model. Experiments were conducted on the China Meteorological Administration Tropical Cyclone Best Track Dataset (CMA) from 2000 to 2020, and the EAR-Interim dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The experimental results show that the proposed model is superior to the deep learning tropical cyclone forecasting methods.

Explore the Mitigation Mechanism of Urban Thermal Environment by Integrating Geographic Detector and Standard Deviation Ellipse (SDE)

The urban surface temperature is a complex integrated natural-human geographic phenomena; with the development of geostatistical methods and the application of multisource data, its research has gradually shifted from a single perspective to a study that integrates multiple factors such as nature and humanity. However, based on the context of the integration of natural and human factors and mutual constraints of each factor, the research on the mechanism of influence on urban habitat thermal environment needs to be further deepened. Therefore, this paper explores the spatial and temporal heterogeneity of urban surface temperature in Zhengzhou City during the summer of 2013–2020 from the perspective of multi-source data fusion, and uses the Geodetector model to quantitatively reveal the main influencing factors of urban surface temperature and the impact of superimposed factors on the compound effect of surface temperature. The results show that: (1) the urban thermal environment in the central of Zhengzhou city (region within the first ring) is obvious, and it is mainly concentrated in commercial and densely populated areas. (2) According to trend analysis, the northwest-southeast direction of the city continues to increase in temperature from 2013–2020, coupled with the direction of urban development. (3) Among the factors affecting urban surface temperature, normalized difference built-up index (NDBI), normalized difference vegetation index (NDVI), tasseled cap wetness (TCW), and human elements are particularly typical. NDVI and TCW are strongly negatively correlated with the urban thermal environment, while NDBI and human elements are strongly positively correlated. (4) Mitigation of the urban thermal environment can start with the interaction mechanism of positive and negative factors. This study provides new ideas for the mechanism analysis of spatial and temporal evolution patterns of the urban thermal environment under multifactorial constraints, and provides suggestions and decisions for promoting green and sustainable urban development.

Thermal Environment Evolution and Response Mechanism of Urban Sprawl Based on Multi-source Data

Improving the urban thermal environment and improving the quality of human settlements are important prerequisites for creating ecologically livable cities. The current research on the relationship between urban expansion and the thermal environment is mostly based on remote sensing data, and the application of multi-source data is weak. Here, we selected the Xi'an metropolitan core area, measured the urban expansion and temporal and spatial evolution of the thermal environment based on Landsat remote sensing images in 2010 and 2020, and used multi-source data, such as interest points and the Baidu thermal index, to study the response mechanism of the urban thermal environment through geoscience statistical analysis methods. The results showed that:① the construction land in the study area had expanded by 200.84 km2, and the area and intensity showed that "the center and the periphery are weaker, and the difference between the two" characteristics, and the expansion mode was mainly edge type and infill type. ② Between 2010 and 2020, the overall thermal environment in the study area deteriorated, and the area of the heat island area increased by 282.65 km2. The spread of the heat island area was in the same direction as the urban expansion, and the distribution pattern evolved from "southeast-northwest" to "northeast-southwest;" however, the average temperature in the central area of the city decreased 1.09℃. ③ Urban expansion was strongly positively correlated with the deterioration of the urban thermal environment. The expansion of urban space had a contribution rate of 60.40% to the deterioration of the thermal environment, and various socio-economic factors had a weaker effect, with an overall contribution rate of 39.60%. The vegetation water body had an obvious cooling effect; under the influence of multiple factors, the surface temperature increased by 0.241 units. In the process of urban expansion, changes in surface parameters and two-dimensional urban morphology were still the main factors for thermal environment changes, whereas three-dimensional morphology had a small effect on the warming of social and economic activities, and the contribution of water bodies and vegetation to cooling was prominent.

Fusing Minimal Unit Probability Integration Method and Optimized Quantum Annealing for Spatial Location of Coal Goafs

Accurately detecting the location of the goafs is an effective method for coordinating underground mining and surface engineering construction and realizing illegal mining supervision. Aiming at the problems of the existing solution methods of goaf spatial characteristic parameters, a spatial location identification method of coal underground goaf with fusing minimal unit probability integration method and optimized quantum annealing is proposed. Meanwhile, to study the characteristics and stability of the proposed model, the advantages and disadvantages of space movement vector data, the robust ability of the method, and the application of multi-source data are discussed in the Discussions. Finally, the achievements of this paper apply to 1414 (1) working face of Gubei Coal Mine in Huainan. The results show the model can accurately identify the location and boundary of the goaf. The research achievements have important theoretical and practical significance for solving problems of land resource reuse in mining areas lacking geological mining data, coal mine safety production, and the supervision of illegal mining.

Design and Development of Maritime Data Security Management Platform

Since the e-Navigation strategy was put forward, various countries and regions in the world have researched e-Navigation test platforms. However, the sources of navigation data are multi-source, and there are still difficulties in the unified acquisition, processing, analysis and application of multi-source data. Users often find it difficult to obtain the required comprehensive navigation information. The purpose of this paper is to use e-Navigation architecture to design and develop maritime data security management platform, strengthen navigation safety guarantee, strengthen Marine environment monitoring, share navigation and safety information, improve the ability of shipping transportation organizations in ports, and protect the marine environment. Therefore, this paper proposes a four-layer system architecture based on Java 2 Platform Enterprise Edition (J2EE) technology, and designs a unified maritime data storage, analysis and management platform, which realizes the intelligent, visualized and modular management of maritime data at shipside and the shore. This platform can provide comprehensive data resource services for ship navigation and support the analysis and mining of maritime big data. This paper expounds on the design, development scheme and demonstration operation scheme of the maritime data security management platform from the system structure and data exchange mode.

Map Customization Service based on User Interest

As for the limitation of cartography technique, printing craft and network technique, map publishing information usually transmits in the unidirectional way, and the publishing cycle lasts long and users cannot participate in cartography, which cannot meet users' needs of individuation. In order to settle these problems, a new map publishing mode which is based on map customization service considering user interest is put forward. Corresponding techniques and solving strategies are discussed, including the manuscript database of map publishing and the application of multi-source data, user interest GIS analysis and disposal, cross-media map color control, the constitute of variable data and the optimal choice of digital printing site, etc. It provides important reference for the convert of map publishing mode from traditional one to map customization service mode based on User Interest. This mode comprehensively uses digital cartography technique, network publishing technique and digital printing technique, which lays emphasis on users' participation in the process of cartography and accords with the thought of individuation service for users.

Land cover classification with multi-source data using evidential reasoning approach

Land cover classification is the core of converting satellite imagery to available geographic data. However, spectral signatures do not always provide enough information in classification decisions. Thus, the application of multi-source data becomes necessary. This paper presents an evidential reasoning (ER) approach to incorporate Landsat TM imagery, altitude and slope data. Results show that multi-source data contribute to the classification accuracy achieved by the ER method, whereas play a negative role to that derived by maximum likelihood classifier (MLC). In comparison to the results derived based on TM imagery alone, the overall accuracy rate of the ER method increases by 7.66% and that of the MLC method decreases by 8.35% when all data sources (TM plus altitude and slope) are accessible. The ER method is regarded as a better approach for multi-source image classification. In addition, the method produces not only an accurate classification result, but also the uncertainty which presents the inherent difficulty in classification decisions. The uncertainty associated to the ER classification image is evaluated and proved to be useful for improved classification accuracy.