Geospatial Data Exploration Using Machine Learning

Geospatial data exploration's major objective is to learn more about spatial characteristics in order to decide accommodation of students in various locations. The K-means technique, which is frequently used in geospatial analysis to find spatial patterns or hotspots of particular phenomena, is...

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Bibliographic Details
Published in2023 4th International Conference on Smart Electronics and Communication (ICOSEC) pp. 1485 - 1489
Main Authors Geetha, K., Srivani, A., Gunasekaran, S., Ananthi, S., Sangeetha, S.
Format Conference Proceeding
LanguageEnglish
Published IEEE 20.09.2023
Subjects
Clustering algorithms
Data exploration
Geology
Geospatial analysis
K-means technique
Machine learning
Student accommodation
Transportation
Urban planning
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Summary:Geospatial data exploration's major objective is to learn more about spatial characteristics in order to decide accommodation of students in various locations. The K-means technique, which is frequently used in geospatial analysis to find spatial patterns or hotspots of particular phenomena, is the algorithm utilised in this research. It is used to organise data points into clusters based their similarity. Urban planning, transportation analysis, and epidemiological research, among other subjects, can all benefit greatly from this kind of analysis. The process of looking into and displaying patterns, trends, and relationships within data that has been gathered and documented using geographic information is known as exploratory analysis of geolocational data. It will be possible to accommodate in this study.
DOI:10.1109/ICOSEC58147.2023.10275920