Smart City Visualization Tool for the Open Data Georeferenced Analysis Utilizing Machine Learning. Estrada, E., Maciel, R., Ochoa, A., Bernabe-Loranca, B., Oliva, D., & Larios, V. International Journal of Combinatorial Optimization Problems & Informatics, 9(2):25–40, May, 2018. ![Smart City Visualization Tool for the Open Data Georeferenced Analysis Utilizing Machine Learning [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper abstract bibtex In Smart cities it is essential the development of information systems that collaborate in the measurement of the urban surroundings towards the cities' sustainability. In this research, for the key performance indicators it is proposed a pattern's visualization of efficiency metrics tool, utilizing the auto learning techniques "machine learning". The objective is to give support to the decision making throughout the georeferenced analysis exploiting the Open Data. The research was applied to the primary public schools data study case, including four stages: the study of metrics, the search of the data model, the test of territorial dependency, and the development of the tool that applies the grouping techniques or clustering to compare the development and school resources by zone. In the tool, the kmeans algorithm is implemented with label as validation method to select the more relevant centroids to display on a map.
@article{estrada_smart_2018,
title = {Smart {City} {Visualization} {Tool} for the {Open} {Data} {Georeferenced} {Analysis} {Utilizing} {Machine} {Learning}},
volume = {9},
issn = {20071558},
url = {http://ezproxy.macewan.ca/login?url=http://search.ebscohost.com/login.aspx?direct=true&db=a9h&AN=128868479&site=ehost-live&scope=site},
abstract = {In Smart cities it is essential the development of information systems that collaborate in the measurement of the urban surroundings towards the cities' sustainability. In this research, for the key performance indicators it is proposed a pattern's visualization of efficiency metrics tool, utilizing the auto learning techniques "machine learning". The objective is to give support to the decision making throughout the georeferenced analysis exploiting the Open Data. The research was applied to the primary public schools data study case, including four stages: the study of metrics, the search of the data model, the test of territorial dependency, and the development of the tool that applies the grouping techniques or clustering to compare the development and school resources by zone. In the tool, the kmeans algorithm is implemented with label as validation method to select the more relevant centroids to display on a map.},
number = {2},
urldate = {2018-09-15TZ},
journal = {International Journal of Combinatorial Optimization Problems \& Informatics},
author = {Estrada, Elsa and Maciel, Rocío and Ochoa, Alberto and Bernabe-Loranca, Beatriz and Oliva, Diego and Larios, Víctor},
month = may,
year = {2018},
keywords = {Clustering for the georeferenced analysis of the Open Data, MACHINE learning, OPEN data movement, SMART cities, Smart City Metrics for the Education Sustainability, Smart City tools},
pages = {25--40}
}
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