Big Geo Data Research Articles

A way for explaining Geo Big Data through its characteristics, sources, and central support technologies

Big Data has significantly impacted scientific research and everyday life, resulting in solutions that align with its principles. Therefore, it is essential to propose ways that enable a basic understanding of specific fields of knowledge, such as those related to spatial domain information. Through a narrative review, this document explains the fundamentals of Geo Big Data by contextualising the characteristics, sources, and central technologies of Big Data in relation to geographic information. One of the most significant findings is that Geo Big Data helps enhance the understanding of biophysical and human dimensions of spatial matters. The technology boosts the acquisition of valuable insights and opportunities across various areas, including risk management, health, agriculture, environment, and open government. This document lays some groundwork for implementing Geo Big Data initiatives towards more specific efforts.

Towards a multi-database CityGML environment adapted to big geodata issues of urban digital twins

Towards a multi-database CityGML environment adapted to big geodata issues of urban digital twins

DKNN: deep kriging neural network for interpretable geospatial interpolation

Geospatial interpolation plays a pivotal role in spatial analysis because it provides high-quality data support for various spatiotemporal data mining (STDM) tasks. However, statistical methods, such as kriging, face challenges in dealing with complex geo-big data. Additionally, deep-learning-based methods, despite their exceptional performance, suffer from limitations, such as poor interpretability. To harness the complementary advantages of these statistical methods and deep learning approaches, this study proposes a novel geospatial artificial intelligence (GeoAI) framework called deep kriging neural network (DKNN). The primary contribution lies in the development of an asymmetric encoder-decoder structure, which includes a deep-learning-based spatial encoder and a geostatistics-based kriging decoder. The spatial encoder consists of three specialized neural networks, whereas the kriging decoder relies on the proposed unified kriging system. During forward propagation, the kriging decoder leverage messages from the spatial encoder to generate interpolation weights for prediction. Conversely, during backward propagation, the kriging decoder guides the spatial encoder in learning interpretable knowledge. Experiments were conducted using both synthetic and practical datasets. The results demonstrate an average improvement of 20.18% in MAE, 25.04% in RMSE and 24.06% in MAPE when compared to the best-performing baseline method. Furthermore, these results confirm the superior interpretability of our DKNN framework.

Sensing the multi-scale landscape functions heterogeneity by big geodata from parcel to urban agglomerations-a case of the Greater Bay Area, China

ABSTRACT Multi-scale landscape functions play a critical role in revealing intricate functional structures within large regions. However, previous studies on landscape functions have predominantly focused on a single macro or micro scale, impeding a holistic multi-scale understanding of the spatial distribution and heterogeneity of landscape functions. To address this gap, this study proposes a framework leveraging the power of big geodata to mine multi-scale landscape functions from parcel to entire urban agglomerations, as well as non-administrative divisions. Our study focuses on the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) in China. Firstly, we integrated multi-source big geodata to derive parcel-scale landscape functions. Subsequently, we employed the Normalized Revealed Comparative Advantage index to derive landscape functions at broader scales, including towns, counties and cities. The effectiveness of our approach is validated through in-field investigations and comparisons with established policy planning positions. The outcomes not only offer distinctive planning insights at various scales but also highlight the versatility of big geodata in extracting landscape functions across scales. This study demonstrates that big geodata is adept at uncovering multi-scale landscape functions irrespective of administrative boundaries, providing valuable insights for fostering multi-scale regional coordinated development.

TELCO GEOBIGDATA FOR THE ANALYSIS OF PRESENCES AND MOVEMENTS: THE 2023 “ADUNATA DEGLI ALPINI” IN UDINE

Abstract. The GEO Big Data phenomenon has become a central topic in the innovation debate. Understanding the presence and movement of individuals within a geographical area is crucial in many fields: urban planning, transport systems, emergencies, tourism, events, … In the last few years a new source of information to reconstruct the dynamics of such complex systems are the data acquired from cellular phone that trace presences and movements with high precision. For privacy issues the data are sold geographically aggregated by TELCO companies. The study of the dynamics of incoming and outgoing, number of trips, number of days and nights spent, number of unique visitors, country of residence and origin, main destination, secondary destination, transit and repeated visits have been analysed for the annual gathering of the ALPINI held in May 2023 in Udine. This work demonstrates how accurate this information is and how it can be used, in addition to analysing the previous edition, to plan locations, logistics, and agendas for future editions.

Spatial Pattern Identification of Urban Leisure Spaces Based on Geo-Big Data and GIS Technology—A Case of Qingdao, China

Accurate understanding urban leisure spaces (ULS) is crucial for optimizing the allocation of leisure resources. This paper aims to provide a detailed classification of ULS and derive its spatial distribution and morphological characteristics using geographical technology. We constructed a hierarchical classification system for leisure functions and identified 11 types of ULS in Qingdao based on the domination and combination of these functions. Single-function spaces are mainly concentrated in the suburbs with relatively large plots, while multi-function zones are primarily distributed in downtown areas with relatively small plots. Natural-recreational leisure spaces are more complex in shape, while cultural-special leisure spaces are less complex. The uneven distribution of different types of leisure spaces suggests that the government should focus on improving natural leisure spaces in downtown areas and developing diversified leisure spaces in the suburban areas.

Big Geodata Reveals Spatial Patterns of Built Environment Stocks Across and Within Cities in China

The patterns of material accumulation in buildings and infrastructure accompanied by rapid urbanization offer an important, yet hitherto largely missing stock perspective for facilitating urban system engineering and informing urban resources, waste, and climate strategies. However, our existing knowledge on the patterns of built environment stocks across and particularly within cities is limited, largely owing to the lack of sufficient high spatial resolution data. This study leveraged multi-source big geodata, machine learning, and bottom-up stock accounting to characterize the built environment stocks of 50 cities in China at 500 m fine-grained levels. The per capita built environment stock of many cities (261 tonnes per capita on average) is close to that in western cities, despite considerable disparities across cities owing to their varying socioeconomic, geomorphology, and urban form characteristics. This is mainly owing to the construction boom and the building and infrastructure-driven economy of China in the past decades. China’s urban expansion tends to be more “vertical” (with high-rise buildings) than “horizontal” (with expanded road networks). It trades skylines for space, and reflects a concentration–dispersion–concentration pathway for spatialized built environment stocks development within cities in China. These results shed light on future urbanization in developing cities, inform spatial planning, and support circular and low-carbon transitions in cities.

Automatic Extraction of the Spatial Distribution of Picea schrenkiana in the Tianshan Mountains Based on Google Earth Engine and the Jeffries–Matusita Distance

As a distinct species in the Tianshan Mountains (TS) of Central Asia (CA), Picea schrenkiana plays a significant role in water purification, soil and water conservation, and climate regulation. In the context of climate change, rapidly and accurately obtaining its spatial distribution has critical decision-making significance for maintaining ecological security in the arid area of CA and the sustainable development of the “Silk Road Economic Belt”. However, conventional methods are extremely challenging to accomplish the high-resolution mapping of Picea schrenkiana in the TS, which is characterized by a wide range (9.97 × 105 km2) and complex terrain. The approach of geo-big data and cloud computing provides new opportunities to address this issue. Therefore, the purpose of this study is to propose an automatic extraction procedure for the spatial distribution of Picea schrenkiana based on Google Earth Engine and the Jeffries–Matusita (JM) distance, which considered three aspects: sample points, remote-sensing images, and classification features. The results showed that (1) after removing abnormal samples and selecting the summer image, the producer accuracy (PA) of Picea schrenkiana was improved by 2.95% and 0.24%–2.10%, respectively. (2) Both the separation obtained by the JM distance and the analysis results of eight schemes showed that spectral features and texture features played a key role in the mapping of Picea schrenkiana. (3) The JM distance can seize the classification features that are most conducive to the mapping of Picea schrenkiana, and effectively improve the classification accuracy. The PA and user accuracy of Picea schrenkiana were 96.74% and 96.96%, respectively. The overall accuracy was 91.93%, while the Kappa coefficient was 0.89. (4) The results show that Picea schrenkiana is concentrated in the middle TS and scattered in the remaining areas. In total, 85.7%, 66.4%, and 85.9% of Picea schrenkiana were distributed in the range of 1500–2700 m, 20–40°, and on shady slope and semi-shady slope, respectively. The automatic procedure adopted in this study provides a basis for the rapid and accurate mapping of the spatial distribution of coniferous forests in the complex terrain.

Quantifying the environmental characteristics influencing the attractiveness of commercial agglomerations with big geo-data

Understanding the attractiveness of commercial agglomerations contributes to urban planning. Existing studies focus less on commercial agglomerations, and most directly use environmental supply factors to characterize attractiveness. This study measures attractiveness from the perspective of human demand. Specifically, we build a novel bipartite graph based on big geo-data of human mobility, using node centralities (degree, betweenness, and pagerank) to measure attractiveness. Next, we summarize multisource environmental features such as Point-of-Interests (POIs), land cover, transportation, and population, and use them as inputs to accurately predict attractiveness based on random forest. Finally, the spatial heterogeneity of the effects of these environmental variables on attractiveness is analyzed by multiscale geographically weighted regression. The results of the Beijing case show that: (1) All three centralities show a trend that the urban center is higher than the surrounding area, and betweenness is more reasonable. (2) Random forest can accurately predict attractiveness, with R2 for degree, betweenness, and pagerank at 0.903, 0.846, and 0.760, respectively. (3) The number of shopping POIs, the length of main roads, and the number of bus stops positively affect attractiveness, while the effects of greening ratio and population density are bidirectional. As for the service scope, about 70% of commercial agglomerations have an average service radius of less than 15 km, which is significantly correlated with the Voronoi diagram. Our results can inspire understanding the human–environment relationship and guide urban policymakers in business planning.

Percolation transitions in urban mobility networks in America's 50 largest cities

Urban mobility can be significantly disrupted by various extreme events. The disruptions threaten urban spatial connectivity and affect people's ability to access various essential services. Accurate characterization and timely alert of the critical transitions of urban mobility networks can help mitigate the above risks. However, there lacks an approach to characterize the critical transition state of urban mobility networks and warn their transitions during extreme events. The universality of the characteristics of disrupted mobility networks across different cities is another fundamental question that remains underexplored. By mining big geodata, we construct the mobility networks of the 50 most populous Metropolitan Statistical Areas (MSAs) in the U.S., and study their disruption patterns by conducting network percolation analysis. We find that all mobility networks experience abrupt transitions when reaching a universal critical threshold, at which the giant components of neighborhoods suddenly collapse and dissolve into small clusters. We also develop an indicator, by analyzing the neighborhood cluster distributions, that approximates how far a mobility network is to the critical threshold and provides an early warning of its critical transition. Our findings provide insights into mobility and neighborhood connectivity in cities, which can provide guidance for transportation management, epidemic control, and emergency evacuation.

Economic risks in the activity of agro-industrial complex enterprises in conditions of import substitution

The scientific article is devoted to the study of economic risks in the activities of enterprises of the agro-industrial complex in the context of import substitution. The purpose of the scientific research is to identify critical risks in the activities of enterprises of the agro-industrial complex in the context of import substitution and to substantiate possible measures to level them. When preparing a scientific publication, general scientific (observation, comparison, measurement, analysis and synthesis, the method of logical reasoning) and specific scientific methods (static analysis, peer review, graphical method, Foresight's "4 worlds" method) were used. Results: the critical risks for the normal functioning of agro-industrial enterprises are: 1) the occurrence of a shortage of seed material; 2) raw material dependence of manufacturers of plant protection products against pests; 3) the critical dependence of the Russian agro-industrial complex on the import of feed additives and vaccines aimed at realizing the genetic potential of animal breeds; 4) degradation of the genetic fund of biological assets bred in animal husbandry with insufficient experience in genetic modeling and domestic selection; 5) technological dependence of the digitalization of business processes in the agro-industrial complex on technology transfer and a decrease in innovative activity in agribusinesses. The main efforts to resolve the existing risks are: active support for domestic seed producers through the provision of tax incentives and other regulatory preferences; creation of a national standard for the formation and storage of data - Big GeoData, which will become the basis for the formation of the domestic market of digital agricultural technologies; creation of full-fledged domestic productions based on gaining access to technologies for the creation of plant protection products and tracking the latest developments by

ЭКОСИСТЕМА ЭЗЕРНЕТА КАК ИНСТРУМЕНТАЛЬНЫЙ БАЗИС ЭНЕРГЕТИЧЕСКОЙ ЭКОНОМИКИ БОЛЬШИХ ДАННЫХ

The future development of the Russian energy sector will be based on the energy economy of big high-frequency data. Russia has begun preparing a new national project to build a big data economy during the year. High-frequency big geodata is a new stage in the development of the digital energy economy. The digital modernization of the Russian energy industry is a key driver of the resource-innovative development of the Russian Big Data economy.

"풍화 산지에서 토사 재해 발생에 영향을 미친 요소들에 대한 연구 - 2020년 충청북도 북부지역을 사례로 -"

Among the reports for damaged area by landslide in Chungju, Jecheon, and Danyang-gun in the northern region of Chungcheongbuk-do, where damage occurred due to heavy rainstorms from July 28 to August 11, 2020, the coordinates of the locations has been confirmed at 438 places. Analyses were conducted for those spots using the landslide information system of the Korea Forest Service, geological information from geobig data, KAKAO map, and satellite images from Google Earth. The most common bedrock in the affected area was granite, followed by gneiss, limestone and other sedimentary rocks, slate and phyllite. As for the slope, sedimentary rocks such as granite and limestone have the steepest slope, and gneiss, granite, slate and phyllite follows it. It seems that the impact by landslide on these types of rocks depended on slope and weathering pattern. The damage type examined in 375 sites out of 438 were due to erosion and sediment runoff from the slope, and debris flow occurred in 63 places. In 31 of them, it was found that the outflow of soil from the slope was connected to the debris flow. As a result of analyzing the rock types, most of the debris flow in the granite area appeared to be ‘runoff-dominant erosion’. It seems that ‘infiltration-triggered soil slip’ was strong in the limestone area. In this study, using Dynamic World, Google’s land cover change tracking service, surface cover changes were tracked between 2018 and 2021-2022 before landslide-related damage occurred, and it was found that small-scale vegetation changes in land use near the study area. It can be seen that changes in Korea’s forestry policy have had a significant impact on this. In particular, significant landslides have occurred after the considerable amount of time followed by anthropogenic change of vegetation and building of forest roads. Therefore, it seems that continuous management is essential to prevent landslide disasters after anthropogenic changes in land cover. In addition, field studies on the condition of soil weathering in mountainous areas and the process of material movement on slopes are essential.

A Multiobjective Land Use Design Framework with Geo-Big Data for Station-Level Transit-Oriented Development Planning

Transit-oriented development (TOD) is among the most feasible strategies for relieving urban issues caused by the unbalanced development of transportation and land use. This study proposes a multiobjective TOD land use design framework for the optimization of the land use layout in station catchments. Given the high density and diverse development in Chinese megacities, a planning model that considers nonlinear impacts on ridership, land use efficiency, quality of life, and the environment is constructed. The model applies fine-grained geo-big data to fill gaps in the empirical and statistical data and improve practicability. A genetic multiobjective optimization approach without reliance on objective weighting is used to generate alternative land use schemes. A metro station in Shanghai is applied as a case study. The results indicate that the proposed ridership objective outperforms the commonly used linear function, and the optimization method has superior extreme optima and convergence to baseline models. We also discuss the consistencies and conflicts in the objectives and provide a balanced land use scheme considering local policies. This work provides suggestions for sustainable urban design with coordinated land use and transportation.

ELULC-10, a 10 m European Land Use and Land Cover Map Using Sentinel and Landsat Data in Google Earth Engine

Land Use/Land Cover (LULC) maps can be effectively produced by cost-effective and frequent satellite observations. Powerful cloud computing platforms are emerging as a growing trend in the high utilization of freely accessible remotely sensed data for LULC mapping over large-scale regions using big geodata. This study proposes a workflow to generate a 10 m LULC map of Europe with nine classes, ELULC-10, using European Sentinel-1/-2 and Landsat-8 images, as well as the LUCAS reference samples. More than 200 K and 300 K of in situ surveys and images, respectively, were employed as inputs in the Google Earth Engine (GEE) cloud computing platform to perform classification by an object-based segmentation algorithm and an Artificial Neural Network (ANN). A novel ANN-based data preparation was also presented to remove noisy reference samples from the LUCAS dataset. Additionally, the map was improved using several rule-based post-processing steps. The overall accuracy and kappa coefficient of 2021 ELULC-10 were 95.38% and 0.94, respectively. A detailed report of the classification accuracies was also provided, demonstrating an accurate classification of different classes, such as Woodland and Cropland. Furthermore, rule-based post processing improved LULC class identifications when compared with current studies. The workflow could also supply seasonal, yearly, and change maps considering the proposed integration of complex machine learning algorithms and large satellite and survey data.

Satellite product to map drought and extreme precipitation trend in Andalusia, Spain: A novel method to assess heritage landscapes at risk

The objective of this work is to develop a methodology for the identification of extreme rain and drought events that have occurred in the last 30 years using products derived from satellite images. Proposed methodology uses statistical reducers such as percentile, drought indexes, and map algebra at a geo big data scale. The daily precipitation data from the Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks-Climate Data Record and Climate Hazards Group InfraRed Precipitation with Station Data were validated by comparison with ground station data. Extreme event maps were obtained from the use of high percentiles. Drought maps were obtained from the standardized precipitation index using low percentiles. The data were migrated to a geographic information system that allows interrelation with other geographic data. Its application to the study of the fortifications preserved in Andalusia classified all structures according to the level of exposure to these dangers and identified two areas of precipitation with different characteristics according to the influence of existing teleconnection patterns. Applied to the study of heritage landscapes, this methodological model minimizes the uncertainty associated with the use of satellite precipitation products, facilitates the planning of preventive conservation activities, and the management of existing resources in occurrence of extreme events.

Revealing spatiotemporal matching patterns between traffic flux and road resources using big geodata - A case study of Beijing

Mismatch between road system and spatiotemporal heterogeneity of traffic flow is a key reason for traffic congestion. Existing studies mainly focus on local regions or specific times (morning and evening peak), while the spatiotemporal heterogeneity of match and its causes, especially at larger scales, are still insufficiently studied. Herein, we proposed a framework for analyzing the match between traffic flux, i.e. the number of individuals driving into or out of a region per unit time, and road resources, using mobile phone data covering approximately 17 million users over one week in Beijing. Matches were measured through comparisons between the share of traffic flux and that of road resources both globally and locally. First, a global analysis with Gini coefficient revealed the match in Beijing is at a long-lasting low level. Then, the spatiotemporal disparity of match was examined via our proposed regional match index. Specifically, overallocated areas (traffic flux exceeds its corresponding shares of road resources) were mainly along arterial roads, while underallocated ones were in suburbs and gated residential communities. To explore the mechanism, four spatiotemporal matching modes were identified through a time series clustering method, and their distributions were explained by urban function based on ‘point-of-interest’ data.

Application of Artificial Neural Networks for Mangrove Mapping Using Multi-Temporal and Multi-Source Remote Sensing Imagery

Mangroves, as unique coastal wetlands with numerous benefits, are endangered mainly due to the coupled effects of anthropogenic activities and climate change. Therefore, acquiring reliable and up-to-date information about these ecosystems is vital for their conservation and sustainable blue carbon development. In this regard, the joint use of remote sensing data and machine learning algorithms can assist in producing accurate mangrove ecosystem maps. This study investigated the potential of artificial neural networks (ANNs) with different topologies and specifications for mangrove classification in Iran. To this end, multi-temporal synthetic aperture radar (SAR) and multi-spectral remote sensing data from Sentinel-1 and Sentinel-2 were processed in the Google Earth Engine (GEE) cloud computing platform. Afterward, the ANN topologies and specifications considering the number of layers and neurons, learning algorithm, type of activation function, and learning rate were examined for mangrove ecosystem mapping. The results indicated that an ANN model with four hidden layers, 36 neurons in each layer, adaptive moment estimation (Adam) learning algorithm, rectified linear unit (Relu) activation function, and the learning rate of 0.001 produced the most accurate mangrove ecosystem map (F-score = 0.97). Further analysis revealed that although ANN models were subjected to accuracy decline when a limited number of training samples were used, they still resulted in satisfactory results. Additionally, it was observed that ANN models had a high resistance when training samples included wrong labels, and only the ANN model with the Adam learning algorithm produced an accurate mangrove ecosystem map when no data standardization was performed. Moreover, further investigations showed the higher potential of multi-temporal and multi-source remote sensing data compared to single-source and mono-temporal (e.g., single season) for accurate mangrove ecosystem mapping. Overall, the high potential of the proposed method, along with utilizing open-access satellite images and big-geo data processing platforms (i.e., GEE, Google Colab, and scikit-learn), made the proposed approach efficient and applicable over other study areas for all interested users.

Revealing Spatiotemporal Matching Patterns between Traffic Flow and Road Resources Using Multisource Big Geodata - a Case Study of Beijing

Revealing Spatiotemporal Matching Patterns between Traffic Flow and Road Resources Using Multisource Big Geodata - a Case Study of Beijing

Development of the digital oil and gas complex in Arctic areas of Russia

This article examines the issues of digital modernization of the oil and gas complex of Russia, the evolution of digital oil and gas technologies. The main results of research work in the field of creating digital technologies for preventing complications and accidents, transferring drilling data in blockchain format, monitoring the state of pipelines using magnetic tomography are briefly outlined. The digital modernization strategy is aimed at large-scale digitalization of oil and gas production facilities and lays a reliable basis for the growth of capital productivity of fields in the long term. The approach is based on improving the quality of management, analyzing the effectiveness of control actions when using an integrated model or a digital twin of the field. The integrated application of digital technologies for effective management is the basis for cost optimization, ensuring the transition to robotic control, and increasing the capital productivity ratio of key gas assets. These solutions are especially effective in regions with difficult natural and climatic conditions or undeveloped infrastructure, Arctic fields. The proposed integrated approach makes it possible to extend the periods of profitable exploitation of gas fields at the stage of declining production and complicated production conditions. By the end of 2025, the number of digital fields using technologies for processing Big Geo Data will exceed 10% of the total number of oil and gas fields in Russia.