Spatial Data Mining Research Articles

A point-feature label placement algorithm based on spatial data mining.

The point-feature label placement (PFLP) refers to the process of positioning labels near point features on a map while adhering to specific rules and guidelines, finally obtaining clear, aesthetically pleasing, and conflict-free maps. While various approaches have been suggested for automated point feature placement on maps, few studies have fully considered the spatial distribution characteristics and label correlations of point datasets, resulting in poor label quality in the process of solving the label placement of dense and complex point datasets. In this paper, we propose a point-feature label placement algorithm based on spatial data mining that analyzes the local spatial distribution characteristics and label correlations of point features. The algorithm quantifies the interference among point features by designing a label frequent pattern framework (LFPF) and constructs an ascending label ordering method based on the pattern to reduce interference. Besides, three classical metaheuristic algorithms (simulated annealing algorithm, genetic algorithm, and ant colony algorithm) are applied to the PFLP in combination with the framework to verify the validity of this framework. Additionally, a bit-based grid spatial index is proposed to reduce cache memory and consumption time in conflict detection. The performance of the experiments is tested with 4000, 10000, and 20000 points of POI data obtained randomly under various label densities. The results of these experiments showed that: (1) the proposed method outperformed both the original algorithm and recent literature, with label quality improvements ranging from 3 to 6.7 and from 0.1 to 2.6, respectively. (2) The label efficiency was improved by 58.2% compared with the traditional grid index.

Generative Artificial Intelligence and Haptic and Biometric Sensor Technologies, Spatial Data Mining and Image Processing Computational Algorithms, and Virtual Team Performance and Behavior Tracking Tools in Immersive Extended Reality Environments

Generative Artificial Intelligence and Haptic and Biometric Sensor Technologies, Spatial Data Mining and Image Processing Computational Algorithms, and Virtual Team Performance and Behavior Tracking Tools in Immersive Extended Reality Environments

Haptic and Biometric Sensor Technologies, Spatial Data Mining and Simulation Modeling Algorithms, and Virtual Navigation and Decision Intelligence Tools across Web3-powered Metaverse Worlds

Haptic and Biometric Sensor Technologies, Spatial Data Mining and Simulation Modeling Algorithms, and Virtual Navigation and Decision Intelligence Tools across Web3-powered Metaverse Worlds

Spatial Data Mining and Image Processing Computational Algorithms, Generative Artificial Intelligence and Multi-Sensory Augmented Reality Systems, and Productivity Software and Emotional State Prediction Tools in Virtual Work Settings

Spatial Data Mining and Image Processing Computational Algorithms, Generative Artificial Intelligence and Multi-Sensory Augmented Reality Systems, and Productivity Software and Emotional State Prediction Tools in Virtual Work Settings

Blockchain-based Decentralized Metaverse Systems, Industrial Artificial Intelligence of Things, and Spatial Data Mining and Acoustic Environment Recognition Algorithms in Realistic 3D Simulation Environments

Blockchain-based Decentralized Metaverse Systems, Industrial Artificial Intelligence of Things, and Spatial Data Mining and Acoustic Environment Recognition Algorithms in Realistic 3D Simulation Environments

Ensembled masked graph autoencoders for link anomaly detection in a road network considering spatiotemporal features

Road anomaly detection aims to find a small group of roads that are exceptional with respect to the rest of the physical links in a transportation network, posing great challenges for spatial data mining and urban infrastructure management applications. Road links involve complex features from both spatial and temporal perspectives, but existing detection methods mostly rely on the topological features of road networks because they face difficulty in capturing the spatial and temporal characteristics of road networks. To overcome this difficulty, we constructed an ensembled autoencoder framework for road anomaly detection by combining spatiotemporal graph learning and graph masking methods, and our main focus was to effectively detect the topological and attribute anomalies mapped by spatiotemporal features in a road network based on traffic flow data. To model local contexts, we performed random masking on the input graph to obtain heterogeneous masked subgraphs. On this basis, a spatiotemporal graph learning model was used to extract topological and traffic flow features from masked subgraphs. Then, we carried out feature reconstructions for each subgraph and calculated the anomaly scores for every node in the graph. Finally, an ensemble model was designed to integrate the anomaly detection results from multiple autoencoders that were trained on the subgraphs to improve feature learning. Our experimental results show that the proposed method is superior to traditional topological analysis methods, machine learning methods (DBScan, isolation forest, LOF, and one-class SVM), and recent deep learning methods (GAE and VGAE).

Understanding Accessibility of Health and Fitness with Big Data Techniques: Facility Visualization in Shanghai with Multi-Source Data

Accessibility of health and fitness services is important for the rehabilitation in the post-epidemic era. The emergence of multi-source data offers new and timely approaches to understanding health and fitness facilities (HFFs) in cities. Taking Shanghai as an example, this paper obtained the HFFs dataset through platforms such as GaoDe, and crawled data were accurately classified into Street level, District level and Municipal level. Based on spatial data mining, the spatial differentiation of current HFFs was understand. The analysis through Kernel Density Estimation (KDE) and the Multi-mode Two-step Floating Catchment Area Method (M2SFCA) indicates that HFFs basically meet the needs of most residents in the city within their service scope. The Lorentz curve results shows that top 20% community residents enjoy up to 73.66% of HFFs. This shows that there is a large difference in HFF accessibility among community residents. This article presents a visualization example of spatial computing in understanding health accessibility and can provide a timely reference for policy making.

Application of Spatial Data Mining in the Process of Discovering Interesting and Previously Unknown, but Potentially Useful, Patterns from Large Spatial Datasets

Spatial data mining has been typically used in the Geographical Information system from physical datasets and locations to real-world events. One of the procedures generally used in capital data mining is vector data representation. Vector data is the most commonly used data across the world. Information in this format consists of tips, angles, and quadrilaterals. It is the simplest method of analysing the data where the vector data consists of tips correlate pairs to indicate a physical location in the world. These points can be joined in a particular way to form closed areas marked as quadrilaterals. Vector data is extremely useful for storing and representing data that has discrete boundaries such as international borders, streets, buildings, and many more. Modern technologies such as Google use geological information and open street maps to represent the data in vector data stricture wise. Keyword : Spatial data mining, Vector data, Information system, Security, Data

Disentangling the contributions of biotic and abiotic predictors in the niche and the species distribution model of Trypanosoma cruzi, etiological agent of Chagas disease

The potential benefits of incorporating biotic, as well as abiotic, predictors in niche and species distribution models (SDMs), as well as how to achieve this, is still debated, with their interpretability and explanatory potential being particularly questioned. It is therefore important to stress test modelling methodologies that include biotic factors against use cases where there is ample knowledge of the potential biotic component of the niche. Relatively well studied and important vector-borne diseases offer just such an opportunity, where knowledge of the agents involved in the transmission cycle –vectors and hosts– can serve to calibrate and test the niche model and corresponding SDM. Here, we study the contributions of biotic –14 vectors, 459 potential hosts– and abiotic –258 climatic categories– predictors to the explanatory and predictive features of the niche and corresponding SDM for the etiological agent of Chagas disease, Trypanosoma cruzi, in Mexico. Using an established spatial data mining technique, we generate biotic, abiotic and biotic+abiotic niche and SDM models. We test our models by comparing predictions of the most important probable hosts of Chagas disease with a previously published list of confirmed hosts. We quantify, compare, and contrast the individual and total contributions of predictors to the niche and distribution of Chagas disease in Mexico. We assess the relative predictive potential of these variables to model performance, showing that models that include relevant biotic niche variables lead to more predictive, more ecologically realistic SDMs. Our research illustrates a useful general procedure for identifying and ranking potential biotic interactions and for assessing the relative importance of biotic and abiotic predictors. We conclude that the inclusion of both abiotic and biotic predictors in SDMs not only provides more predictive and accurate models but also models that are more understandable and explainable from an ecological niche perspective.

Application of Spatial Data Mining to national mines inventories for exploration and land planning of high place-value mineral resources. The case of aggregates in Spain

Countries' mine inventories usually include information related to exploited rock type or mineral, amount of resources, owner, location, mining rights associated, etc. All this information is a vast dataset that in the last couple of decades has been included in digital cartography in official websites, some of them freely available. This information is usually exploited as individual registers classified by substance, location, etc., but when analyzed as a whole and combined with other economic, social, and environmental variables, valuable information can be obtained. If the variable time is included in the study, these databases can reveal the evolution of mining activity in recent decades.This study analyzes the distribution of aggregates quarries in Spain, as a case study, relating their location to different socio-geographical features such as urban areas, environmentally protected areas, or population density, by means of Spatial Data Mining (SDM) tools. Aggregates are used as construction materials and are the largest non-energy extractive industry, being the second most consumed product by human beings after water. Due to the low unit value and high volume of consumption, aggregates are considered a high place-value commodity. By combining the information of aggregates in the Spanish mines inventory with geological, social, demographic, environmental, and administrative information, and using SDM techniques, relevant conclusions can be obtained such as the delineation of areas in the land use planning to be dedicated to aggregates exploitation, or the identification of priority zones for exploration for mining companies. This information could serve policy makers and land use planners in making data-driven decisions and mining companies in determining areas with the highest profit potential to develop their operations.

Knowledge discovery of Middle East dust sources using Apriori spatial data mining algorithm

Identifying the areas susceptible to dust storm formation is one effective way of dealing with this destructive environmental phenomenon. This study is the first attempt to employ the Apriori spatial data mining algorithm to dust source susceptibility mapping (DSSM). The research process was based on extracting association rules between spatial-temporal patterns of dust drivers (including soil, vegetation, and climate parameters) in the Middle East's hotspots dust sources (HDSs). For this purpose, HDSs were identified using visual interpretation of sub-daily MODIS-Terra/Aqua RGB images from 2000 to 2021. The Middle East's HDSs mainly correspond to desert areas with poor vegetation cover and ephemeral/dried-up water bodies. A total of three million rules were extracted by running the Apriori algorithm. Accordingly, bare and non-vegetated lands, high soil thickness, low soil moisture, very high wind speed, and high temperature were identified as the most common features of HDSs. Using three measures including support, confidence, and lift, 54 frequent, reliable, and logical rules were selected, and the related maps were generated. Then, the susceptible dust sources (SDSs) map of the Middle East was produced in five classes of extreme (13% of the areas), high (14%), moderate (16%), low (17%), and no (40%) susceptibility through the weighted linear combination of the rule maps. The accuracy of the identified SDSs was estimated at 83.7% using the verification points. A sensitivity analysis was performed using the leave-one-out method to determine the isolated effect of the selected rules on the produced SDSs map. The model uncertainty varied between 15.7% and 16.8% for different rules. The variation range of uncertainty was 1.1%, demonstrating that a single rule does not significantly affect the model's performance; however, some rules have a more influential role. Our results revealed that Apriori's ability to provide generalizable association rules is a robust algorithm for DSSM.

Evaluation Method of Product Shape Features based on Multidimension Spatial Data Mining

Analysis of product shape design has attracted more and more attention of researchers because it can make products better meet perceptual needs. Based on the theory of spatial data mining, this study proposes an evaluation method of a product shape design scheme. The method proposed in this study not only obtains the shape design of the overall and local features of the product, which solves the problem of the insufficient utilization of spatial data by the analysis method. During the simulation process, the model obtains the product shape design and appeal from the online evaluation spatial data, which can integrate the product perceptual knowledge in the spatial data, which greatly reduces the manual operation steps and the required time for the degree of data utilization. The experimental results show that after the obtained data are filtered to extract the feature words, the weight of the feature words is calculated by the TF-IDF method, the number of neighbors is increased from 1 to 30, the interval is 5, and the vectorized representation of the spatial data is constructed. The similarity between the calculation sentences of the data mining method is 89.7%, which effectively improves the support function and design efficiency of spatial data mining for product design.

Crime forecasting and prevention using spatial data mining

This paper focuses on finding safe spot and hotspots using spatial data mining and predicting crime. spatial data mining was applied to a real data set in Los Angeles, and here the study took upon itself the application of the block algorithm using the mean block algorithm K in two stages, the first stage is clustering was performed on the geographical data represented as two features, longitude and latitude, on the coordinates and here represents the spatial data, which is the focus of the study, the result which extract from this stage is , obtain labeled class and to find clusters in the given spatial data in a separate data frame for the coordinators, the second stage, used the K-mode cluster algorithm on the (CRM_DESC, WEAPON_DESC, PREMIS_DESC) classified the crime based on weapon used, premise and crime description to extract classified data under separate crime type data. The last step is to integrate the data extracted from the first stage with the data extracted from the second stage to know the distribution of crimes over Los Angeles regions and counties, in addition to supporting by reviewing the extent of the spread of crimes.

Multi-temporal Satellite Images and Combination of Multi-Classification Approaches for Evaluation of Changes in Forest Cover: A case study

The primary objective of this study is to develop a method to monitor the changes in forest cover using three different techniques, namely Spatial Data Mining (SDM), supervised and unsupervised classification, and Geographical Information System (GIS) overlay processing methods. The secondary objective was to monitor the land use changes using multi-temporal satellite images (Landsat 8 images of 2016 and 2020). The study area is located in Nuwara Eliya, Sri Lanka. The first and second approaches consisted of supervised and unsupervised classification approaches. The use of a combination of two classification mapping approaches provides better results compared with the use of a single classification, which is a novelty of the present study. The GIS overly processing technique combined the two maps obtained from supervised and unsupervised classifications to develop an improved map for land use changes. The improved map was reclassified and converted into ASCII (American Standard Code for Information Interchange) format, which was then pre-processed to convert the data set into the suitable format for the SDM modeling. The converted format was used to implement SDM modeling with the support of the Clustering-outline detection algorithm. The overall accuracy of the Landsat 8 images was 94.6. Results revealed that the forest cover extent was diminished by 5.28% in the study area between 2016 and 2020. The ground measurement was done with the help of the forest department to verify the results. The study revealed that the forest area decreased, and farming lands increased due to intensive agriculture. Future studies would be necessary to determine the validity and suitability of the developed model for other climatic zones in the country.

Product Shape Design Scheme Evaluation Method Based on Spatial Data Mining

The stage of product modeling design implies a lot of complex tacit knowledge, which is the embodiment of the design concept centered on product modeling design and is also the hot spot and difficulty of modern design theory and method research. Aiming at the evaluation and decision of product modeling design scheme, a decision-making method of approaching ideal solution ranking based on grey relational analysis was proposed, which realized the convergence of tacit knowledge. The empty association rule is an important knowledge content of spatial data mining. A fuzzy genetic algorithm can solve the characteristics of random and nonlinear problems and solve the data mining problems of spatial association rules. The fuzzy genetic algorithm of discrete crossover probability and mutation probability is applied to data mining of spatial association rules in a spatial database, the coding method of the fuzzy genetic algorithm and the construction of fitness function are discussed, and the process of mining spatial association rules is given. The results show that the method of mining s association rules with the fuzzy genetic algorithm is feasible and has higher mining efficiency. This paper discusses the construction method of designing a decision support database based on linear regression and neural network and then proposes a decision method combining TOPSIS and grey relational analysis, which comprehensively considers the position and shape of the scheme data curve.

The use of spatial data mining methods for modeling HR challenges of generation Z in greater Poland Region

The use of spatial data mining methods for modeling HR challenges of generation Z in greater Poland Region

Multimedia Technology of Spatial Data Mining Based on Genetic Algorithm.

In order to make key decisions more conveniently according to the massive data information obtained, a spatial data mining technology based on a genetic algorithm is proposed, which is combined with the k-means algorithm. The immune principle and adaptive genetic algorithm are introduced to optimize the traditional genetic algorithm, and the K-means, GK, and IGK algorithms are compared and analyzed. The results show that, in two different datasets, the objective functions obtained by the K-means algorithm are 94.05822 and 4.10373 (×106), respectively, while the objective functions obtained by the GK and IGK algorithms are 89.8619 and 3.9088 (×106), respectively. The difference between the three algorithms can also be reflected in the data comparison of the number of iterations. The number of iterations required for k-means to reach the optimal solution is 8.21 and 8.4, respectively, which is the most among the three algorithms, while the number of iterations required for IGK to reach the optimal solution is 5.84 and 4.9, respectively, which is the least. Although the time required for K-means is short, by comparison, the IGK algorithm we use can get the optimal solution in relatively less time.

Overview on the Development of Intelligent Methods for Mineral Resource Prediction under the Background of Geological Big Data

In the age of big data, the prediction and evaluation of geological mineral resources have gradually entered a new stage, intelligent prospecting. This review briefly summarizes the research development of textual data mining and spatial data mining. It is considered that the current research on mineral resource prediction has integrated logical reasoning, theoretical models, computational simulations, and other scientific research models, and has gradually advanced toward a new model. This type of new model has tried to mine unknown and effective knowledge from big data by intelligent analysis methods. However, many challenges have come forward, including four aspects: (i) discovery of prospecting big data based on geological knowledge system; (ii) construction of the conceptual prospecting model by intelligent text mining; (iii) mineral prediction by intelligent spatial big data mining; (iv) sharing and visualization of the mineral prediction data. By extending the geological analysis in the process of prospecting prediction to the logical rules associated with expert knowledge points, the theory and methods of intelligent mineral prediction were preliminarily established based on geological big data. The core of the theory is to promote the flow, invocation, circulation, and optimization of the three key factors of “knowledge”, “model”, and “data”, and to preliminarily constitute the prototype of intelligent linkage mechanisms. It could be divided into four parts: intelligent datamation, intelligent informatization, intelligent knowledgeization, and intelligent servitization.

A technique for Spatial Data Classification Method Using Random Forest based Correlation


 
 
 Spatial data mining is a technique mainly used for predictive analytics. Data mining is the method of determining necessary samples from spatial datasets using machine learning methods. But, the existing prediction methods failed to forecast accurately with more accuracy and reduced error. A Random Forest Correlation based Fuzzy classification technique is introduced to improve the spatial data classification and error reduction. The algorithm constructs a random forest and the correlation is measured between the training and testing features. A fuzzy rule based classification is performed for classification into subsets. The proposed method takes forest fire dataset as input and evaluates the parameters such as classification accuracy, space complexity and classification time. The output is a subset of classes of fired and non fired region with enhanced classification accuracy, minimal false-positive rate and reduced time complexity.
 
 
 
 
 
 
 
 

STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity

Spatial clustering has been widely used for spatial data mining and knowledge discovery. An ideal multivariate spatial clustering should consider both spatial contiguity and aspatial attributes. Existing spatial clustering approaches may face challenges for discovering repeated geographic patterns with spatial contiguity maintained. In this paper, we propose a Spatial Toeplitz Inverse Covariance-Based Clustering (STICC) method that considers both attributes and spatial relationships of geographic objects for multivariate spatial clustering. A subregion is created for each geographic object serving as the basic unit when performing clustering. A Markov random field is then constructed to characterize the attribute dependencies of subregions. Using a spatial consistency strategy, nearby objects are encouraged to belong to the same cluster. To test the performance of the proposed STICC algorithm, we apply it in two use cases. The comparison results with several baseline methods show that the STICC outperforms others significantly in terms of adjusted rand index and macro-F1 score. Join count statistics is also calculated and shows that the spatial contiguity is well preserved by STICC. Such a spatial clustering method may benefit various applications in the fields of geography, remote sensing, transportation, and urban planning, etc.