Spatial Objects Research Articles

A quantitative fuzzy-valued intersection matrix for obtaining fuzzy relationships between vague spatial objects

Topological relation between geographical objects is one of the key components of the Geographical Information System (GIS). Considering the objects to be exact with sharp boundaries, four intersection, and nine intersection matrices initiated the study of calculating the topological relations. Due to the extensive existence of vague spatial phenomenons, the study of uncertainty object modeling has grown rapidly. Parallelly, the relationship calculating tools have been upgraded to handle vague concepts. The fuzzy 9-intersection matrix and Egg-Yolk methods are popular tools. Although the geographical objects are uncertain, surprisingly, most of these tools generate certain relations between them. The qualitative nature of these tools overlooks the essence of their uncertainty. We propose a quantitative fuzzy-valued 9-intersection matrix to overcome these drawbacks to obtain a fuzzy relationship between uncertain geographical objects. Then two new similarity computation techniques are introduced to calculate the membership grade between the proposed matrix and the known crisp matrices. These similarity computations allow two spatial objects to have partial membership against the eight established topological relations. The quantitative calculations indicate the strength of the relationship. The superiority of the proposed model is exhibited through comparative studies. Further, certain linguistic variables are linked to the evaluated membership grades to generate an immediate association with the known crisp relations. Examples are provided to demonstrate the applicability and efficacy of this association.

A self-powered biomimetic mouse whisker sensor (BMWS) aiming at terrestrial and space objects perception

The extremely sensitive whisker discernment of mice is the key expertise enabling their survival in extreme adverse environments. Previous sensors mimicking animal whiskers for environmental awareness tend to be cumbersome, reliant on external power, and limited application scenarios, which constrains their applicability in more extensive contexts. Inspired by the ability of mice exploring and perceiving environments with their whiskers, a lightweight self-powered BMWS is designed for terrestrial and spatial objects exploration. Owing to the merits of triboelectric and decent structural design, BMWS demonstrates outstanding collision detection capabilities and signal stability. Subsequently, BMWS showcases its extraordinary strength in tasks including direction identification, hole width discrimination, real-time distance sensing, and intelligent early warning. Empowered by an artificial neural network (ANN), BMWS accomplishes the identification of ground and spatial objects with astounding accuracy, pioneering the transfer application of ANN to the domain of biomimetic mouse whiskers and expanding the research boundaries of biomimetic mouse whisker sensors, while taking the first step toward whisker sensor deployment in space environments.

Detailed Modeling and Visualization of Spatial Objects Based on BIM - Implementation Example of Bathroom Component with Detailed Reflection on the Physical Characteristics of the Elderly -

Detailed Modeling and Visualization of Spatial Objects Based on BIM - Implementation Example of Bathroom Component with Detailed Reflection on the Physical Characteristics of the Elderly -

SOCF: A correlation filter for real-time UAV tracking based on spatial disturbance suppression and object saliency-aware

The discriminative correlation filter (DCF) is commonly used in aerial object tracking due to its high tracking accuracy and computing speed. However, when similar object disturbances emerge in the background, the response map will generate sub-peaks, which may eventually lead to tracking failure. Meanwhile, the lack of attention to the tracked object can also cause tracking performance degradation. To these concerns, this paper proposes a novel correlation filter algorithm for real-time aerial tracking based on spatial disturbance suppression and object saliency-aware, i.e., SOCF. Firstly, this paper designs a novel spatial disturbance suppression strategy. Using the temporal information in the historical response maps, we construct a context response map, deviating it from the current response map to detect disturbance information in the background. Then, construct a spatial interference map, divide it into n×n non-overlapping regions, and suppress the negative samples in the disturbance region within the main regression. Furthermore, an object saliency-aware strategy is proposed, using a saliency detection algorithm to calculate the object-aware mask and multiplying it with the detection filter to obtain the object-aware filter. By constructing the object-aware regularization in the training phase, the trained detection filter focuses more on the object itself and can effectively separate the object from the background. Extensive experiments on four widely used unmanned aerial vehicle (UAV) datasets demonstrate that the proposed SOCF tracker achieves high tracking performance. Meanwhile, our tracker can maintain real-time aerial tracking at 48 FPS on a single CPU.

Designing residential areas using geospatial modelling

The subject of the paper is design using modelling methods of geographic information system (GIS). The authors propose using GIS modelling as a method to solve the actual problem of designing housing estates in a big city. The purpose of the paper is to design a model of a residential area with a complete infrastructure and all necessary elements. In the paper, the following tasks are solved: 1) developing design of a digital geoinformation model of a projected residential area; 2) formalization of the process of designing residential areas by developing a design algorithm; 3) creation of a visual two-dimensional model of a residential area. The method used in the paper is geoinformation modelling of real estate objects using two-dimensional models in a geodatabase, and software implementation using the ArcMap application. As a result of the research, the authors obtained the following results: a centric approach to planning a semi-autonomous region was chosen as the most rational and effective for urban planning. To solve the problem, the geographic information system ArcGIS was used, and a geodatabase was created to analyse the existing territory of the city of Kharkiv and select a construction site. The developed geographic information system ArcGIS and the created geodatabase help in solving the issues of further designing the location of buildings and infrastructure elements of the area. Namely, “buffer zones” were used for further accommodation of schools, kindergartens, and shops. The use of “buffer zones” made it possible to optimally place these establishments depending on the number of potential visitors. The authors chose blocks for the division and development of the projected area, considering the historical aspects of the city of Kharkiv. This enabled to develop a GIS in which each block would have its urban ecosystem. The authors have also developed an algorithm for performing design tasks of residential areas. The algorithm can be applied when creating projects for residential areas not only in Kharkiv, but also in other cities of Ukraine and the world. Results of the research made it possible to use the capabilities of geoinformation systems in designing new types of residential areas with a highly developed social and transport infrastructure, harmonious development, as well as attractive features for stakeholders and future residents. Thus, the developed GIS visualization will enable a visual representation on the map of all spatial objects, which are necessary for analysis, and to reflect spatial patterns of the placement of all necessary infrastructure facilities.

Enhanced Point Cloud Slicing Method for Volume Calculation of Large Irregular Bodies: Validation in Open-Pit Mining

The calculation of volumes for irregular bodies holds significant relevance across various production processes. This spans tasks such as evaluating the growth status of crops and fruits, conducting morphological analyses of spatial objects based on volume parameters, and estimating quantities for earthwork and excavation. While methods like drainage, surface reconstruction, and triangulation suffice for smaller irregular bodies, larger ones introduce heightened complexity. Technological advancements, such as UAV photogrammetry and LiDAR, have introduced efficient point cloud data acquisition methods, bolstering precision and efficiency in calculating volumes for substantial irregular bodies. Notably, open-pit mines, characterized by their dynamic surface alterations, exemplify the challenges posed by large irregular bodies. Ensuring accurate excavation quantity calculations in such mines is pivotal, impacting operational considerations, acceptance, as well as production cost management and project oversight. Thus, this study employs UAV-acquired point cloud data from open-pit mines as a case study. In practice, calculating volumes for substantial irregular bodies often relies on the point cloud slicing method. However, this approach grapples with distinguishing multi-contour boundaries, leading to inaccuracies. To surmount this hurdle, this paper introduces an enhanced point cloud slicing method. The methodology involves segmenting point cloud data at fixed intervals, followed by the segmentation of slice contours using the Euclidean clustering method. Subsequently, the concave hull algorithm extracts the contour polygons of each slice. The final volume calculation involves multiplying the area of each polygon by the spacing and aggregating these products. To validate the efficacy of our approach, we employ model-derived volumes as benchmarks, comparing errors arising from both the traditional slicing method and our proposed technique. Experimental outcomes underscore the superiority of our point cloud volume calculation method, manifesting in an average relative error of 1.17%, outperforming the conventional point cloud slicing method in terms of accuracy.

MODERN METHODOLOGICAL MEANS OF TEACHING GRAPHIC DISCIPLINES FOR FIRST-YEAR STUDENTS

In today's complex social conditions, significant changes are taking place in the system of domestic higher education. High-quality graphic training of graduates of higher educational institutions has been a pressing problem for many decades, so it is necessary to systematically update the existing methodological base and develop effective forms of training. As you know, drawing is the international language of architects and engineers. Working with flat images of spatial objects requires students to have developed spatial thinking already in the first year. In .this study, detailed attention is paid to the review and analysis of existing methodological approaches to the teaching of important classical sections of the discipline "Descriptive Geometry". The trends of improving teaching under the condition of distance learning are also considered, which helps future specialists of creative specialties to increase the level of individual competitiveness in modern market conditions. The purpose of the research is to determine and improve modern methodological tools for teaching graphic disciplines of students of the first course of creative specialties. Our study is related to the scientific research work "Improving the organization of the educational process using distance learning methods and teaching methods of graphic disciplines for students of construction and architectural and art specialties based on the competence approach", which is carried out on the basis of the Department of "Descriptive Geometry and Engineering Graphics" Odessa State Academy of Construction and Architecture (OSACEA) for many recent years. The work uses theoretical and empirical research methods: analysis, classification and synthesis of research base sources, many years of personal teaching experience; diagnostics of students specializing in 191 Architecture and Urban Planning and 023 Fine Arts (observations, monitoring the quality of teaching, self-analysis, etc.). Graphic competencies are very significant for a graduate’s future professional activity, which gives freedom in the creative implementation of the entire design process from the birth of an idea to its implementation.

Immunohistochemical Characteristics of Glial Cells Reorganization in Neocortex as a Result of Severe Traumatic Brain Injury

The aim was to study the reorganization of gliocytes and GFAP expression of the rat neocortex using histological, immunomorphological and morphometric methods (in particular fractal analysis) after severe traumatic brain injury (STBI).Material and methods. Modeling of STBI was performed using a patented device with a shock mechanism based on the principle of transferring kinetic energy of a falling cylindrical load. During the experiment, the rats were divided into 2 groups: main (n=30) and control (intact, n=6). The brain of rats was extracted after 1, 3, 7, 14 and 30 days after heavy traumatic brain injury. The assessment of the glia of layers III and V of the sensorimotor cortex (SMC) was carried out by analyzing histological preparations of the rat brain (stained with hematoxylin-eosin and thionin) using light microscopy, immunohistochemical reaction to glial fibrillary acid protein (GFAP) and morphometric processing in the Image 1.53 program. Fractal analysis was performed using the FracLac 2.5 plugin to evaluate the astrocyte network. Statistical processing of the data obtained was carried out using nonparametric methods of information processing of the application package Statistica 10.0 programs.Results. Throughout the post-traumatic period, changes in the SMC neurons were accompanied by an increase in the numerical density of gliocytes. The maximum peak of the microglyocyte content was higher already 1 day after HTBI: by 13.4% in layer III of the SMC and by 24.2% in layer V of the SMC. The minimum indicator of the numerical density of oligodendrocytes was observed 30 days after HTBI: in layer III of the SMC by 32.2%, and in layer V of the SMC – 43.9%. 1 day after the injury, there was a statistically significant increase in the numerical density of astrocytes in layers III and V of the SMC above the control values. During 30 days, the content of gliocytes decreased: in layer III of the SMC – 23%, and in layer V of the SMC – 26.8%. When GFAP was expressed, protoplasmic and fibrous astrocytes differing in body size and length of processes were found in layer III and V of the SMC. The indicators characterizing the appendages as a spatial object changed statistically significantly in comparison with the control and in the post-traumatic period between terms. The fractal dimension changed the most and a high lacunarity was observed in layer III of the SMC. For 14 days, the indicator was less than the reference value, and by this time it was already recovering in layer V of the SMC. Taken together, all this indicated a higher reactive reorganization of the processes of astrocytes of the small cell layer of the III SMC.Conclusion. After a heavy traumatic brain injury, a heterogeneous reorganization of the neuroglial complex was observed in the sensorimotor cortex (SMC). The detected changes in the numerical density of gliocytes in layers III and V of the SMC throughout the post-traumatic period were heterochronous. Morphometric analysis data confirmed the spatial reorganization of astrocytes, active proliferation of microglyocytes and oligodendrocytes – changes that contribute to the compensatory protection of the structures of the nervous tissue of the SMC in the post-traumatic period.

A Comprehensive Model Incorporating Multiple Spatial Relations in 3D Space

Aims: A Comprehensive Model Incorporating Multiple Spatial Relations in 3D Space. Background: At present, the research on two-dimensional spatial relation expression and inference models is relatively mature, but these models cannot be used to deal with three-dimensional spatial orientation relations. With the application of spatial orientation relations, threedimensional spatial orientation relations are involved in many fields such as urban architectural design, robotics, image processing, etc. Two-dimensional spatial orientation relations models cannot satisfy the needs of three-dimensional spatial applications, so there is an urgent need to research three-dimensional spatial object orientation relations expression and inference models. Objective: This work aims to provide a comprehensive model incorporating multiple spatial relations in 3D space. The joint representation of direction, distance, and topological relations describes more complex spatial position relations among objects. Methods: Based on this comprehensive model, the computational properties of interval algebra are used to combine the directional and topological relations. Result: The study lays a good foundation for the formal representation and reasoning of spatial relations between regions, enhances the analyzability of spatial relations between objects, and improves the accuracy of spatial analysis. Conclusion: The main novel contribution of this paper is that we propose a comprehensive orientation relation model, called 3D-TRD, which considers three spatial orientation relations simultaneously. The paper gives examples to represent the position relations of two spatial objects by comparing the RCC8 model, the 3DR46 model, and the comprehensive model to highlight the advantages of our proposed model. Based on the model, the bidirectional mapping association method is also used to represent the location of the spatial objects. The first advantage of the 3DTRD model is that it represents spatial location relations more accurately than 3DR46, RCC8, and five qualitative distances. The second advantage of the 3D-TRD model is that it proposes a bidirectional mapping representation in three-dimensional space. The third advantage of the 3DTRD model is that it provides a good basis for the formal representation and inference study of the spatial relations between regions.

ModularImageAnalysis (MIA): Assembly of modularised image and object analysis workflows in ImageJ.

ModularImageAnalysis (MIA) is an ImageJ plugin providing a code-free graphical environment in which complex automated analysis workflows can be constructed and distributed. The broad range of included modules cover all stages of a typical analysis workflow, from image loading through image processing, object detection, extraction of measurements, measurement-based filtering, visualisation, and data exporting. MIA provides out-of-the-box compatibility with many advanced image processing plugins for ImageJ including Bio-Formats, DeepImageJ, MorphoLibJ and TrackMate, allowing these tools and their outputs to be directly incorporated into analysis workflows. By default, modules support spatially calibrated 5D images, meaning measurements can be acquired in both pixel and calibrated units. A hierarchical object relationship model allows for both parent-child (one-to-many) and partner (many-to-many) relationships to be established. These relationships underpin MIA's ability to track objects through time, represent complex spatial relationships (e.g. topological skeletons) and measure object distributions (e.g. count puncta per cell). MIA features dual graphical interfaces: the "editing view" offers access to the full list of modules and parameters in the workflow, while the simplified "processing view" can be configured to display only a focused subset of controls. All workflows are batch-enabled by default, with image files within a specified folder being processed automatically and exported to a single spreadsheet. Beyond the included modules, functionality can be extended both internally, through integration with the ImageJ scripting interface, and externally, by developing third-party Java modules that extend the core MIA framework. Here we describe the design and functionality of MIA in the context of a series of real-world example analyses. This article is protected by copyright. All rights reserved.

Spatial objects classification using machine learning and spatial walk algorithm

Abstract This article presents a novel method for classifying spatial objects by learning node representations via a spatial walk algorithm. The findings show that considering both the attributes of objects and their topological relationships enables more efficient and precise spatial objects’ classification than methods that only consider the objects’ characteristics. The method emphasizes the importance of spatial dependencies in learning representations for spatial data. A distinctive feature of the method is its focus on local analysis of the neighborhood structure of the node under investigation. The spatial walk algorithm offers a defined path generation scheme, facilitating a deeper understanding of local spatial dependencies between objects. This approach provides a more accurate representation of the essential relationships between spatial objects than random path generation and enhances the classification results, as demonstrated in three different classification scenarios. The method proves particularly effective in the context of spatial objects, where proximity and a limited number of neighbors play a significant role. This is exemplified in the classification of planning areas in spatial development plans.

Декомпозиция ретроспективных топографических карт для послойной оценки типов регионального природопользования

On the example of the most developed part of the lake Baikal basin, the decomposition of the digital coverage content from 12 sheets of the topographic map scale 1:84 000, compiled by the Corps of Military Topographers in 1896-1912, was performed. As a result of vectorization of retrospective layers of topographic objects and subsequent transformation of their shp files into classes of spatial objects of the ArcGIS environment, two cartographic sets of spatial object classes were formed that characterize two types of regional environmental management - traditional pastoral and invasive agricultural. As a result of layer-by-layer geoinformation modeling of cartographic sets on a modern topographic basis and a digital relief model, primary areas of settlement and economic use of the territory were established, physical and geographical characteristics of the past economic use of the territory were determined, and separate mapping of spatial and temporal life support structures of different types of nature management was performed. For operational digital visualization, a technological solution is proposed for storing and mapping sets of spatial object classes by organizing a file-based geodata of the ArcGIS information environment. Cartographic reconstructions of pastoral and agricultural nature management at the beginning of the 20th century are presented, as well as a general cartographic reconstruction of nature management on a landscape basis, which allows us to assert that traditional pastoral nature management does not involve the transformation of the Earth's surface and is characterized by continuous restorative dynamics of natural landscapes, and invasive nature management is based on the violation of soil cover, which later led to the emergence of areas of water and wind erosion.

IDENTIFIKASI KARAKTERISTIK SPATIAL QUESTIONS MAHASISWA PENDIDIKAN GURU SEKOLAH DASAR PADA MATERI GEOMETRI

This research aims to identify the characteristics of spatial questions of PGSD students based on a taxonomy of spatial thinking. This type of research is descriptive research with subjects as many as 22 students selected from 120 PGSD students at Trunojoyo University based on certain criteria. The results of the research were 560 spatial questions which were created by research subjects after being given 6 spatial image objects. The characteristics of the 560 spatial questions based on the taxonomy of spatial thinking are: 414 (73,93%) at primitive spatial level, 99 (17,68%) at simple spatial level, 22 (3,92%) at complex spatial level, 10 (1,79%) non-spatial level. These results conclude that the spatial questions characteristics of the subject tend to be at the primitive spatial level which leads to lower cognitive questions. Other research findings included 15 (2,68%) spatial questions that were not accommodated in the taxonomy of spatial thinking, giving rise to indications of the addition of a new level.

Роль русских и китайских урбонимов в коммуникативном пространстве современного мегаполиса

The ways of nominating modern spatial realities reflect linguistic conscious-ness and create a new linguistic reality, therefore, the analysis of the names of objects that are part of the communicative space becomes an urgent problem. The article analyzes the ways of explication of toponyms, in particular antonyms, in the Russian and Chinese communicative space, the relationship between the naming of the object and the cultural code.
 The process of nomination of modern spatial realities reflects the linguistic consciousness and creates a new linguistic reality, so the analysis of locus names (toponyms) is an urgent problem. The article studies the ways of explication of space, the interrelation of language and cultural codes. The aim of the study was to describe the ways of explication of lexical units (paronyms) in Russian and Chinese, to determine their functionality in the communicative space of the metropolis.
 To achieve the goal, the following methods were used: continuous sampling method, comparative etymological and linguoculturological analyses.
 As a result of the analysis of the names of spatial objects, in particular the urban names of the Moscow and Beijing metro, their classification based on etymology, the authors come to the conclusion that lexical units that name spatial objects perform not only informational, cultural, communicative, but also culturally preserving functions, that is, they are a way of preserving cultural patterns that affect the atmosphere of the communicative space of the metropolis.
 The scientific novelty of the study lies in the fact that for the first time a comparative description of the urban names of the Moscow and Beijing metro is given, some ways of verbalization of urban objects are revealed and their role in the communicative space is determined.
 The practical significance lies in the fact that the analysis of the semantics of spatial lexical units helps to understand the trends in the process of nominating loci, their influence on the formation of a special communicative space, and also the fact that, giving a name to an urban object, we fill it with special information that is passed on to future generations.

Spatial graph attention network-based object tracking with adaptive cosine window

Spatial graph attention network-based object tracking with adaptive cosine window

Разработка системы полуавтоматического моделирования трехмерных изображений

Descriptive geometry is one of the main disciplines of the general engineering cycle. It presents methods of accurate representation of spatial objects on a plane, as well as the identification of geometric shapes of figures from given images. That is, this discipline is designed to give future engineers the knowledge and skills to build and read drawings.
 By causing the intensive work of students' spatial thinking, descriptive geometry contributes to the development of spatial representation. In descriptive geometry, a flat image of a spatial object is called a plot (drawing).
 Students studying descriptive geometry get acquainted with examples of practical use of the theoretical provisions of descriptive geometry in course and diploma design.
 However, it is much easier to perceive drawings in volume if they can be rotated and viewed from different sides. For these purposes, you can not do without third-party software. Visualization of objects in the presence of a three-dimensional model is provided by Unity. But to create a three–dimensional model according to the drawing, other software is required - for example, Blender. Therefore, the authors set a goal: to create a semi-automatic visualization system for two-dimensional drawings in space based on Blender software and to develop comprehensive and simple documentation that will allow even people far from programming to use the program.
 It is known that Blender is a powerful program for creating three-dimensional graphics that allows you to create various objects, scenes, animations, as well as edit them. Blender is a free and open source program available to anyone interested in three-dimensional modeling and computer graphics creation.
 Among the Blender tools for creating three-dimensional models are powerful tools for modeling, sculpting, animation and visualization of objects and scenes. In Blender, you can also draw textures and create 3D animations using animation tools such as key frames.

Orbán Placed in Europe: Ukraine, Russia and the Radical-Right Populist Heartland

ABSTRACT Radical-right populism (RRP) is notably based on the production of antagonism involving the ‘people’, the ‘elite’ and ‘others’. The analysis of RRP discourse can be particularly useful to investigate RRP geopolitics as the representation of an international situation legitimising national and foreign policies. Based on an analysis of narratives relating to Ukraine and Russia produced by Viktor Orbán, the RRP Prime Minister of Hungary, the current article researches a key spatial object associated with the people and justifying Orbán’s position concerning the two countries between 2014 and 2022: the heartland. This analysis shows that Orbán refused to choose his ‘camp’ between Russia and Ukraine over many years, preferring to position himself as a defender of a people’s heartland, the location of which changes depending on his addresses and temporal contexts. The defence of this heartland generally involves the naming of a clear enemy: the Western liberal world.

Implementing a Digital Twin of an Underground Utility Tunnel for Geospatial Feature Extraction Using a Multimodal Image Sensor

The increasing interest in digital twin technology, the digitalization of worn-out social overhead capital (SOC), and disaster management services has augmented the usage of 3D spatial models and information to manage infrastructure. In this study, a digital twin of a subterranean utility tunnel was created, and spatial objects were identified using inbuilt image sensors. The novelty lies in the development of a unique algorithm that breaks down the structure of the utility tunnel into points, lines, and planes, identifying objects using a multimodal image sensor that incorporates light detection and ranging (LiDAR) technology. The three main conclusions of this study are the following: First, a digital twin of the utility tunnel was constructed using building information modeling integrated with a geographic information system (BIM-GIS). Second, a method for extracting spatial objects was defined. Third, image-sensor-based segmentation and a random sample consensus (RANSAC) were applied. In this process, the supplementary algorithm for extracting and updating 3D spatial objects was analyzed and improved. The developed algorithm was tested using point cloud data, showing easier object classification with more precise LiDAR data.

Qualitative Reasoning about 2D Cardinal Directions using Answer Set Programming

We introduce a formal framework (called NCDC-ASP) for representing and reasoning about cardinal directions between extended spatial objects on a plane, using Answer Set Programming (ASP). NCDC-ASP preserves the meaning of cardinal directional relations as in Cardinal Directional Calculus (CDC), and provides solutions to all consistency checking problems in CDC under various conditions (i.e., for a complete/incomplete set of basic/disjunctive CDC constraints over connected/disconnected spatial objects). In particular, NCDC-ASP models a discretized version of the consistency checking problem in ASP, over a finite grid (rather than a plane), where we provide new lower bounds on the grid size to guarantee that it correctly characterizes solutions for the consistency checking in CDC. In addition, NCDC-ASP has the following two novelties important for applications. NCDC-ASP introduces default CDC constraints to represent and reason about background or commonsense knowledge that involves default qualitative directional relations (e.g., "the ice cream truck is by default to the north of the playground" or "the keyboard is normally placed in front of the monitor"). NCDC-ASP introduces inferred CDC constraints to allow inference of missing CDC relations and to provide them as explanations. We illustrate the uses and usefulness of NCDC-ASP with interesting scenarios from the real-world. We design and develop a variety of benchmark instances, and comprehensively evaluate NCDC-ASP from the perspectives of computational efficiency.

Sustainable Development Goal 6 Assessment and Attribution Analysis of Underdeveloped Small Regions Using Integrated Multisource Data

Data scarcity is a key factor impacting the current emphasis on individual indicators and the distribution of large-scale spatial objects in country-level SDG 6 research. An investigation of progress assessments and factors influencing SDG implementation in cities and counties indicates that smaller-scale regions hold greater operational significance for achieving the 2030 Agenda for Sustainable Development from the bottom up; thus, urgent attention should be given to data deficiencies and inadequate analyses related to SDG impact attribution. This study, conducted in the National Innovative Demonstration Zone for Sustainable Development of Lincang City, investigates multisource data sources such as integrated statistics, survey data, and remote sensing data to analyze the progress and status of SDG 6 achievement from 2015–2020, and employs the LMDI decomposition model to identify influential factors. The assessment results demonstrate that the SDG 6 composite index in Lincang increased from 0.47 to 0.61 between 2015 and 2020. The SDG 6 indicators and SDG 6 composite index have significant spatial heterogeneity. The water resources indexes in wealthy countries are high, the water environment and water ecology indexes in developing countries are comparatively high, and the SDG 6 composite index is high in undeveloped counties. Technological and economic advances are the main positive drivers impacting the SDG 6 composite index, and the relative contributions of technology, economy, structure, and population are 61.84%, 54.16%, −4.03%, and −11.96%, respectively. This study shows that integrated multisource data can compensate for the lack of small-scale regional statistical data when quantitative and comprehensive multi-indicator evaluations of the SDGs are conducted. And, policies related to SDG 6.1.1, SDG 6.2.1, and SDG 6.3.1 can be a priority for implementation in undeveloped regions with limited funding.