A model of geographic information system using graph clustering methods

Setiadi, Tedy; Pranolo, Andri; Aziz, Muhammad; Mardiyanto, Sukrisno; Hendrajaya, Bayu; Munir, -

doi:10.1109/icsitech.2017.8257208

Cited by 7 publications

(3 citation statements)

References 12 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Networks have been used to represent census data for clustering purposes (Dias and Nonato 2015; Setiadi et al. 2017), but these works did not explore temporal evolution, where they are particularly powerful. Networks allow a natural representation of these inconsistent regions, with both spatial and temporal connections.…”

Section: Related Workmentioning

confidence: 99%

Neighborhood Dynamics with Unharmonized Longitudinal Data

Dias

Silver

2019

Geographical Analysis

View full text Add to dashboard Cite

This article proposes a novel method for data-driven identification of spatiotemporal homogeneous regions and their dynamics, enabling the exploration of their composition and extents. Using a simple network representation, the method enables temporal regionalization without the need for geographical harmonization. To allow for a transparent corroboration of our method, we use it as a basis for an interactive and intuitive interface for the progressive exploration of the results. The interface guides the user through the original data, enabling both experts and nonexperts to characterize broad patterns of stability and change and identify detailed local processes. The proposed methodology is suitable for any region-based data, and we validate our method with illustrative scenarios from Chicago and Toronto, with results that match the established literature. The system is publicly available, with demographic data for over forty regions in the USA and Canada between 1970 and

show abstract

Section: Related Workmentioning

confidence: 99%

Neighborhood Dynamics with Unharmonized Longitudinal Data

Dias

Silver

2019

Geographical Analysis

View full text Add to dashboard Cite

show abstract

“…Graphs were used to represent census data for clustering purposes before [21], [26], but these works did not explore temporal evolution, where graphs are particular powerful as they allow a natural representation of inconsistent regions, with both spatial and temporal connections. Note that there are other possible representations that have similar properties, but we adopted graphs to allow the use of the existing literature and methods.…”

Section: Data Representationmentioning

confidence: 99%

Visualizing demographic evolution using geographically inconsistent census data

Dias¹,

Silver²

2018

Preprint

View full text Add to dashboard Cite

Video Abstract: https://youtu.be/bKeV08Os0uA Census measurements provide reliable demographic data going back centuries. However, their analysis is often hampered by the lack of geographical consistency across time. We propose a visual analytics system that enables the exploration of geographically inconsistent data. Our method also includes incremental developments in the representation, clustering, and visual exploration of census data, allowing an easier understanding of the demographic groups present in a city and their evolution over time. We present the feedback of experts in urban sciences and sociology, along with illustrative scenarios in the USA and Canada.

show abstract

“…Each clustering method may have advantages and disadvantages according to the study objective, size, type of data, number of clusters, and type of software used [13]. In most environmental studies, density-based clustering is the most common approach used because the data can be spatially represented on a physical level in several forms, using a raster format (grids cells or pixels) or vector format (point, lines, or polygons) [17]. The spatial data can then be compared and analyzed using various interpolation methods, such as kriging [18,19], in order to find where the data are closer together.…”

Section: Introductionmentioning

confidence: 99%

Using a Sensitivity Analysis and Spatial Clustering to Determine Vulnerability to Potentially Toxic Elements in a Semiarid City in Northwest Mexico

et al. 2022

View full text Add to dashboard Cite

The Getis-Ord Gi* statistic clustering technique was used to create a hot spot exposure map using 14 potentially toxic elements (PTEs) found in urban dust samples in a semiarid city in northwest Mexico. The dust distribution and deposition in this city are influenced by the seasonal wind and rain from the North American Monsoon. The spatial clustering patterns of hot spots were used in combination with a sensitivity analysis to determine which variables most influenced the PTE hot spot exposure base map. The hot spots areas (%) were used as indicators of environmental vulnerability, and a final integrated map was selected to represent the highest vulnerability of PTEs with a 99% level of confidence. The results of the sensitivity analysis indicated that the flood zones and pervious and impervious zones were the most sensitive variables due to their weight in the spatial distribution. The hot spot areas were reduced by 60.4% by not considering these variables. The hot spot analysis resulted in an effective tool that allowed the combination of different spatial layers with specific characteristics to determine areas that present greater vulnerability to the distribution of PTEs, with impacts on public and environmental health.

show abstract

A model of geographic information system using graph clustering methods

Cited by 7 publications

References 12 publications

Neighborhood Dynamics with Unharmonized Longitudinal Data

Neighborhood Dynamics with Unharmonized Longitudinal Data

Visualizing demographic evolution using geographically inconsistent census data

Using a Sensitivity Analysis and Spatial Clustering to Determine Vulnerability to Potentially Toxic Elements in a Semiarid City in Northwest Mexico

Contact Info

Product

Resources

About