Machine Learning Approaches in Spatial Data Mining

Shalini Bhaskar Bajaj; Ashima Narang; Priyanka Vashisth

doi:10.55524/ijircst.2024.12.2.25

Abstract

This review paper surveys the integration of machine learning techniques in spatial data mining, a crucial intersection of geographic information systems and data mining. It examines the application of various machine learning algorithms such as classification, regression, clustering, and deep learning in spatial data analysis. The paper discusses challenges like data preprocessing, feature selection, and model interpretability, alongside recent advancements including spatial-temporal analysis and heterogeneous data integration. Through critical analysis of existing literature, it identifies trends, methodologies, and future research directions. Practical implications and applications across domains like urban planning, environmental monitoring, and epidemiology are explored. As a comprehensive resource, this review facilitates understanding and utilization of machine learning approaches for extracting insights from spatial data, benefiting researchers, practitioners, and policymakers alike.

Keywords

Spatial Data Mining, Machine Learning, Geographic Information Systems, Classification, Clustering, Spatial-Temporal Analysis, Data Integration

References

L. Anselin, “Spatial Econometrics: Methods and Models,” Springer, 2019.
Y. Chen et al., “Urban Scene Understanding via Graph Convolutional Networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019.
J. Choi et al., “Deep Learning-Based Arrhythmia Detection from Ambulatory Electrocardiography Data,” Sensors, vol. 21, no. 3, p. 842, 2021.
J. Gao et al., “A Deep Learning Approach to Detect DDoS Attacks Using Convolutional Neural Networks,” Future Generation Computer Systems, vol. 102, pp. 1122-1131, 2020.
X. Jia et al., “Anomaly Detection in Water Quality Monitoring Data Using Machine Learning Techniques,” Environmental Science and Pollution Research, vol. 26, no. 31, pp. 31680-31691, 2019.
X. Li et al., “Deep Learning-Based Air Quality Prediction in Urban Areas,” Environmental Pollution, vol. 242, pp. 608-618, 2018.
Y. Li et al., “Hierarchical Clustering Analysis of Urban Growth Patterns Using Remote Sensing Data,” Remote Sensing, vol. 12, no. 7, p. 1146, 2020.
T. Y. Lin et al., “Single Shot Multibox Detector,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2017.
W. Liu et al., “Land Cover Classification Using K-Means Clustering in Urban Areas,” Remote Sensing, vol. 11, no. 8, p. 896, 2019.
Z. Shen et al., “Predicting Future Land Use Changes Using Machine Learning Techniques,” Land Use Policy, vol. 90, p. 104305, 2020.
H. Wang et al., “Anomaly Detection of Dengue Fever Transmission Using DBSCAN Clustering,” International Journal of Environmental Research and Public Health, vol. 15, no. 11, p. 2434, 2018.
S. Yang et al., “Machine Learning-Based Prediction of Dengue Fever Spread in Urban Areas,” International Journal of Environmental Research and Public Health, vol. 16, no. 3, p. 443, 2019.
J. Zhang et al., “Deep Learning-Based Land Cover Classification in Urban Areas Using High-Resolution Satellite Imagery,” Remote Sensing, vol. 12, no. 6, p. 932, 2020.
M. Ankerst et al., “OPTICS: Ordering Points to Identify the Clustering Structure,” ACM SIGMOD Record, vol. 28, no. 2, pp. 49-60, 1999.
L. Breiman, “Random Forests,” Machine Learning, vol. 45, no. 1, pp. 5-32, 2001.
N. Cressie, “Statistics for Spatial Data,” Wiley, 2015.
B. S. Everitt et al., “Cluster Analysis,” Wiley, 2011.
J. H. Friedman, “Greedy Function Approximation: A Gradient Boosting Machine,” Annals of Statistics, vol. 29, no. 5, pp. 1189-1232, 2001.M. F.
Goodchild et al., “Foundations of Geographic Information Science,” CRC Press, 2007.Hastie, T., et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction”, Springer, 2009
K. He et al., "Deep Residual Learning for Image Recognition," in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016.
B. Lantz, "Machine Learning with R," Packt Publishing Ltd., 2013.
Y. LeCun et al., "Deep Learning," Nature, vol. 521, no. 7553, pp. 436-444, 2015.
T. M. Mitchell, "Machine Learning," McGraw Hill, 1997.
K. P. Murphy, "Machine Learning: A Probabilistic Perspective," MIT Press, 2012.
F. Pedregosa et al., "Scikit-learn: Machine Learning in Python," Journal of Machine Learning Research, vol. 12, pp. 2825-2830, 2011.
Raschka et al., "Python Machine Learning," Packt Publishing Ltd., 2015.
B. Schölkopf et al., "Estimating the Support of a High-Dimensional Distribution," Neural Computation, vol. 13, no. 7, pp. 1443-1471, 2001.
R. Tibshirani, "Regression Shrinkage and Selection via the Lasso," Journal of the Royal Statistical Society: Series B (Methodological), vol. 58, no. 1, pp. 267-288, 1996.
I. H. Witten et al., "Data Mining: Practical Machine Learning Tools and Techniques," Morgan Kaufmann, 2016

Cites this article as

S. B. Bajaj, A. Narang, P. Vashisth, "Machine Learning Approaches in Spatial Data Mining", International Journal of Innovative Research in Engineering & Management (ijircst), Vol-12, Issue-2, Page No-140-148, 2024. Available from: https://doi.org/10.55524/ijircst.2024.12.2.25

Corresponding Author

Shalini Bhaskar Bajaj

Professor, Department of Computer Science & Engineering, Amity University, Gurugram, Haryana, India

Download Full Paper

Download PDF

No. of Downloads: 55 | No. of Views: 987

Advancing Education through VR: The Emerging Scope of Virtual Classrooms

Mohd Tanveer, Mohd Azat, Inayat Husain, Sakil Ahmad, Mohammad Aalam Khan.

May 2025 - Vol 13, Issue 3
Speech Recognition Technologies: Design, Challenges, and Real-World Applications

Maruti Maurya, Mohd Zaheer, Nawab Mohammad, Sadaf siddiqui, Mohd Zeeshan Khan, Mohd Ayan Akram.

May 2025 - Vol 13, Issue 3
Deep Learning-based Sentiment Analysis of Text using Long Short-Term Memory Networks

MD Shahid Ali, Saif Ali , Abdullah Parwez, Abu Sufiyan, Mohd Haroon.

May 2025 - Vol 13, Issue 3

Machine Learning Approaches in Spatial Data Mining

Citations

Download Full Paper PDF

Total View 987

Total Download 55