Machine Learning Approaches in Spatial Data Mining (original) (raw)
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World Journal of Advanced Research and Reviews, 2024
In this era of technological innovation, the integration of machine learning techniques with Geographic Information Systems (GIS) has emerged as a transformative approach to spatial analysis and decision-making. This abstract explores the synergy between machine learning and GIS, highlighting their combined potential to unlock new insights from spatial data, automate analytical processes, and enhance predictive modeling capabilities. By leveraging machine learning algorithms such as neural networks, random forests, and support vector machines, GIS practitioners can address complex spatial challenges more effectively, ranging from land cover classification and urban growth modeling to environmental monitoring and disaster response. Through case studies and examples, this abstract demonstrates the practical applications of machine learning in GIS, illustrating its role in advancing our understanding of spatial phenomena and informing evidence-based decision-making across diverse domains. As the field continues to evolve, embracing the fusion of machine learning and GIS holds immense promise for unlocking the full potential of spatial analysis and shaping a more sustainable and resilient future. Illustrative case studies and examples showcase the practical applications of machine learning in GIS across diverse domains. From land cover classification and urban growth modeling to environmental monitoring and disaster response, machine learning algorithms offer versatile solutions to address a wide spectrum of spatial challenges. Through the fusion of machine learning and GIS, researchers and practitioners gain unprecedented insights into complex spatial phenomena, enabling them to make data-driven decisions that are both informed and actionable. Looking ahead, the fusion of machine learning and GIS holds immense promise for advancing our understanding of spatial dynamics and shaping a more sustainable and resilient future. As the field continues to evolve, embracing this interdisciplinary approach is essential for unlocking the full potential of spatial analysis, fostering innovation, and addressing pressing societal challenges at local, regional, and global scales. By leveraging the synergies between machine learning and GIS, we can chart a path towards a more data-driven, informed, and equitable world.
An Empirical Study of Deep Learning Strategies for Spatial Data Mining
2020
The emergence of scalable frameworks for machine learning to efficiently analyse and derive valuable insights from these data has triggered growing volumes of data collected. Huge spatial data frameworks cover a wide variety of priorities, including tracking of infectious diseases, simulation of climate change, etc. Conventional mining techniques, especially statistical frameworks to handling these data, are becoming exhausted due to the rise in the number, volume and quality of spatio-temporal data sets. Various machine learning tasks have recently shown efficiency with the development of deep learning methods. We therefore include a detailed survey in this paper on important impacts in the application of deep learning techniques to the mining of spatial data. Keyword: Big data, Convolutional Neural Network, Deep Learning, Machine Learning, Spatial Data Mining.
Machine Learning of Spatial Data
ISPRS International Journal of Geo-Information, 2021
Properties of spatially explicit data are often ignored or inadequately handled in machine learning for spatial domains of application. At the same time, resources that would identify these properties and investigate their influence and methods to handle them in machine learning applications are lagging behind. In this survey of the literature, we seek to identify and discuss spatial properties of data that influence the performance of machine learning. We review some of the best practices in handling such properties in spatial domains and discuss their advantages and disadvantages. We recognize two broad strands in this literature. In the first, the properties of spatial data are developed in the spatial observation matrix without amending the substance of the learning algorithm; in the other, spatial data properties are handled in the learning algorithm itself. While the latter have been far less explored, we argue that they offer the most promising prospects for the future of spa...
The Geographical Society of North-Eastern Hill Region, Department of Geography, N.E.H.U., Shillong 793022, 2024
This study delves deep into how Artificial Intelligence (AI) and Big Data can revolutionize geography. We explore how these technologies enhance spatial analysis and decision support systems, leading to more accurate, efficient, and sustainable outcomes (Smith, 2021). By integrating AI and Big Data, we facilitate improved data acquisition and management, enabling the processing of large volumes of geospatial data with ease (Garcia et al., 2020). This empowers geographers to conduct advanced spatial analyses and modeling, unveiling intricate patterns and predicting trends with unprecedented accuracy. Furthermore, we highlight the diverse applications of AI and Big Data in geography, from optimizing urban development to monitoring the environment and preparing for disasters (Brown, 2023). However, alongside these advancements come challenges, such as data privacy concerns, potential biases in algorithms, and issues with data quality and integration (Gupta & Patel, 2021). Also, we emphasize the imperative of interdisciplinary collaborations and the development of user-friendly tools and platforms to democratize the adoption of AI and Big Data applications in geography (Lee & Kim, 2020). By working together across different fields and simplifying access to these technologies, we can ensure that everyone can contribute to and benefit from the transformative potential of AI and Big Data in geography, and can call this era are Geography 2.0 Era
Deep Learning Techniques for Geospatial Data Analysis
ArXiv, 2020
Consumer electronic devices such as mobile handsets, goods tagged with RFID labels, location and position sensors are continuously generating a vast amount of location enriched data called geospatial data. Conventionally such geospatial data is used for military applications. In recent times, many useful civilian applications have been designed and deployed around such geospatial data. For example, a recommendation system to suggest restaurants or places of attraction to a tourist visiting a particular locality. At the same time, civic bodies are harnessing geospatial data generated through remote sensing devices to provide better services to citizens such as traffic monitoring, pothole identification, and weather reporting. Typically such applications are leveraged upon non-hierarchical machine learning techniques such as Naive-Bayes Classifiers, Support Vector Machines, and decision trees. Recent advances in the field of deep-learning showed that Neural Network-based techniques ou...
Unveiling the Potential of Machine Learning Applications in Urban Planning Challenges
Land
In a digitalized era and with the rapid growth of computational skills and advancements, artificial intelligence and Machine Learning uses in various applications are gaining a rising interest from scholars and practitioners. As a fast-growing field of Artificial Intelligence, Machine Artificial Intelligence deals with smart designs, data mining and management for complex problem-solving based on experimental data on urban applications (land use and cover, configurations of the built environment and architectural design, etc.), but with few explorations and relevant studies. In this work, a comprehensive and in-depth review is presented to discuss the future opportunities and constraints in meeting the next planning portfolio against the multiple challenges in urban environments in line with Machine Learning progress. Bringing together the theoretical views with practical analyses of cases and examples, the work unveils the huge potential, but also the potential barriers of the comp...
Data-Driven Analytical Techniques in Geographic Information Systems
ISRES Publishing, 2024
This study explores how Geographic Information Systems (GIS) can be enhanced through the integration of big data, data science, and machine learning techniques. It highlights the role of data-driven approaches in overcoming the limitations of traditional GIS methods.This provides new dimensions to spatial data analysis. The study focuses on the utilization of machine learning and deep learning techniques for processing and analyzing big data obtained from various sources such as satellite imagery, sensors, and social media. Application areas such as urban planning, disaster management, environmental monitoring, and transportation analysis are discussed as examples. Additionally, the study examines the advantages of integrating augmented reality (AR), real-time data analytics, and cloud-based solutions to GIS. These technologies are shown to have significant potential in areas such as city planning, traffic management, and monitoring environmental changes. The importance of data visualization tools and techniques in facilitating the interpretation of spatial data and supporting decision-making processes is emphasized. Finally, the study addresses existing challenges, including data quality, integration issues, and high computational costs, while discussing future trends such as AI-powered models and cloud-based solutions.
The role of artificial neural network and machine learning in utilizing spatial information
Spatial Information Research
In this age of the fourth industrial revolution 4.0, the digital world has a plethora of data, including the internet of things, mobile, cybersecurity, social media, forecasts, health data, and so on. The expertise of machine learning and artificial intelligence (AI) is required to soundly evaluate the data and develop related smart and automated applications, These fields use a variety of machine learning techniques including supervised, unsupervised, and reinforcement learning. The objective of the study is to present the role of artificial neural networks and machine learning in utilizing spatial information. Machine learning and AI play an increasingly important role in disaster risk reduction from hazard mapping and forecasting severe occurrences to real-time event detection, situational awareness, and decision assistance. Some of the applications employed in the study to analyze the various ANN domains included weather forecasting, medical diagnosis, aerospace, facial recognition, stock market, social media, signature verification, forensics, robotics, electronics hardware, defense, and seismic data gathering. Machine learning determines the many prediction models for problems involving classification, regression, and clustering using known variables and locations from the training dataset, spatial data that is based on tabular data creates different observations that are geographically related to one another for unknown factors and places. The study presents that the Recurrent neural network and convolutional neural network are the best method in spatial information * Akash Goel
ISPRS International Journal of Geo-Information
Urban systems involve a multitude of closely intertwined components, which are more measurable than before due to new sensors, data collection, and spatio-temporal analysis methods. Turning these data into knowledge to facilitate planning efforts in addressing current challenges of urban complex systems requires advanced interdisciplinary analysis methods, such as urban informatics or urban data science. Yet, by applying a purely data-driven approach, it is too easy to get lost in the ‘forest’ of data, and to miss the ‘trees’ of successful, livable cities that are the ultimate aim of urban planning. This paper assesses how geospatial data, and urban analysis, using a mixed methods approach, can help to better understand urban dynamics and human behavior, and how it can assist planning efforts to improve livability. Based on reviewing state-of-the-art research the paper goes one step further and also addresses the potential as well as limitations of new data sources in urban analytic...
Machine learning is a computational technology widely used in regression and classification tasks. One of the drawbacks of its use in the analysis of spatial variables is that machine learning algorithms are in general, not designed to deal with spatially autocorrelated data. This often causes the residuals to exhibit clustering, in clear violation of the condition of independent and identically distributed random variables. In this work we analyze the performance of some well-established machine learning algorithms and one spatial algorithm in the prediction of the average rent price of certain real estate units in the Miami-Fort Lauderdale-West Palm Beach metropolitan area in Florida, USA. We defined "performance" as the goodness of fit achieved by an algorithm in conjunction with the degree of spatial association of the residuals. We identified significant differences between the machine learning algorithms in their sensitivity to spatial autocorrelation and the achieved goodness of fit. We also exposed the superiority of machine learning algorithms over generalized least squares in both goodness of fit and residual spatial autocorrelation. Finally we show preliminary evidence that blending ensemble learning can be used to optimize a regression problem. Our findings can be useful in designing a strategy for regression of spatial variables.