Mining the City Data: Making Sense of Cities with Self-Organizing Maps

2011 ◽  
pp. 168-177 ◽  
Author(s):  
Omar Neme ◽  
J R G Pulido ◽  
Antonio Neme
Keyword(s):  
Self Organizing Maps ◽  
Making Sense ◽  
The City ◽  
Self Organizing

Clustering of City Housing Facilities Based on Self-Organizing Maps

2015 ◽  
Vol 725-726 ◽  
pp. 1057-1062 ◽  
Author(s):  
Tatiana Simankina ◽  
Olga Popova
Keyword(s):  
Housing Stock ◽  
Network Modeling ◽  
Self Organization ◽  
Neural Network Modeling ◽  
Self Organizing Maps ◽  
High Level ◽  
The City ◽  
Regional Programs ◽  
Housing Facilities ◽  
Self Organizing

The algorithm for clustering based on neural network modeling using T. Kohonen's self-organizing maps for the analysis of the housing stock is considered. This analysis of housing stock is required for the planning of complex reproduction of housing and major repairs regional programs development. The mechanism of self-organization is submitted. The representative sample clustering of the housing stock is produced. Its result is 16 groups of objects with a high level of internal similarity. The basic advantages of this approach for monitoring and analysis of the city housing stock are described.

scholarly journals Analysis of Wetland Landcover Change in Great Lakes Urban Areas Using Self-Organizing Maps

2021 ◽  
Vol 13 (24) ◽  
pp. 4960
Author(s):  
Elissa Penfound ◽  
Eric Vaz
Keyword(s):  
Ecosystem Services ◽  
Great Lakes ◽  
Urban Areas ◽  
Flood Mitigation ◽  
Wetland Ecosystem ◽  
Wetland Area ◽  
Self Organizing Maps ◽  
Landcover Change ◽  
The City ◽  
Self Organizing

Wetland loss and subsequent reduction of wetland ecosystem services in the Great Lakes region has been driven, in part, by changing landcover and increasing urbanization. With landcover change data, digital elevation models (DEM), and self-organizing maps (SOM), this study explores changing landcover and the flood mitigation attributes of wetland areas over a 15-year period in Toronto and Chicago. The results of this analysis show that (1) in the city of Toronto SOM clusters, the landcover change correlations with wetland volume and wetland area range between −0.1 to −0.5, indicating that a more intense landcover change tends to be correlated with small shallow wetlands, (2) in the city of Chicago SOM clusters, the landcover change correlations with wetland area range between −0.1 to −0.7, the landcover change correlations with wetland volume per area range between −0.1 to 0.8, and the landcover change correlations with elevation range between −0.2 to −0.6, indicating that more intense landcover change tends to be correlated with spatially small wetlands that have a relatively high water-storage capacity per area and are located at lower elevations. In both cities, the smallest SOM clusters represent wetland areas where increased landcover change is correlated with wetland areas that have high flood mitigation potential. This study aims to offer a new perspective on changing urban landscapes and urban wetland ecosystem services in Toronto and Chicago.

A Psychographic Segmentation of Kuwaiti Travelers Using Self-Organizing Maps

2019 ◽  
Vol 24 (1) ◽  
pp. 87-92 ◽  
Author(s):  
Yvette Reisinger ◽  
Mohamed M. Mostafa ◽  
John P. Hayes
Keyword(s):  
Self Organizing Maps ◽  
Self Organizing

Self Organizing Maps, Pattern Recognition and Financial Crises

2011 ◽  
Author(s):  
Sylvain Barthelemy ◽  
Pascal Devaux ◽  
Francois Faure ◽  
Matthieu Pautonnier
Keyword(s):  
Pattern Recognition ◽  
Financial Crises ◽  
Self Organizing Maps ◽  
Self Organizing

Using self organizing maps to analyze larval fish assemblage vertical dynamics through environmental-ontogenetic gradients

2021 ◽  
pp. 107410
Author(s):  
I. Álvarez ◽  
J.S. Font-Muñoz ◽  
I. Hernández-Carrasco ◽  
C. Díaz-Gil ◽  
P.M. Salgado-Hernanz ◽  
...  
Keyword(s):  
Fish Assemblage ◽  
Larval Fish ◽  
Self Organizing Maps ◽  
Self Organizing

scholarly journals The Spread of the COVID-19 Outbreak in Brazil: An Overview by Kohonen Self-Organizing Map Networks

Medicina ◽  
2021 ◽  
Vol 57 (3) ◽  
pp. 235
Author(s):  
Diego Galvan ◽  
Luciane Effting ◽  
Hágata Cremasco ◽  
Carlos Adam Conte-Junior
Keyword(s):  
The Self ◽  
Self Organizing Map ◽  
Virus Spread ◽  
Self Organizing Maps ◽  
The North ◽  
Som Clustering ◽  
Unsupervised Neural Networks ◽  
Mining Tools ◽  
Temporal Spread ◽  
Self Organizing

Background and objective: In the current pandemic scenario, data mining tools are fundamental to evaluate the measures adopted to contain the spread of COVID-19. In this study, unsupervised neural networks of the Self-Organizing Maps (SOM) type were used to assess the spatial and temporal spread of COVID-19 in Brazil, according to the number of cases and deaths in regions, states, and cities. Materials and methods: The SOM applied in this context does not evaluate which measures applied have helped contain the spread of the disease, but these datasets represent the repercussions of the country’s measures, which were implemented to contain the virus’ spread. Results: This approach demonstrated that the spread of the disease in Brazil does not have a standard behavior, changing according to the region, state, or city. The analyses showed that cities and states in the north and northeast regions of the country were the most affected by the disease, with the highest number of cases and deaths registered per 100,000 inhabitants. Conclusions: The SOM clustering was able to spatially group cities, states, and regions according to their coronavirus cases, with similar behavior. Thus, it is possible to benefit from the use of similar strategies to deal with the virus’ spread in these cities, states, and regions.

scholarly journals Geospatial Analysis of Extreme Weather Events in Nigeria (1985–2015) Using Self-Organizing Maps

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Adeoluwa Akande ◽  
Ana Cristina Costa ◽  
Jorge Mateu ◽  
Roberto Henriques
Keyword(s):  
Extreme Precipitation ◽  
Large Scale ◽  
Northern Region ◽  
Extreme Weather Events ◽  
Self Organizing Map ◽  
Spatial And Temporal Patterns ◽  
Self Organizing Maps ◽  
The North ◽  
Precipitation Regimes ◽  
Self Organizing

The explosion of data in the information age has provided an opportunity to explore the possibility of characterizing the climate patterns using data mining techniques. Nigeria has a unique tropical climate with two precipitation regimes: low precipitation in the north leading to aridity and desertification and high precipitation in parts of the southwest and southeast leading to large scale flooding. In this research, four indices have been used to characterize the intensity, frequency, and amount of rainfall over Nigeria. A type of Artificial Neural Network called the self-organizing map has been used to reduce the multiplicity of dimensions and produce four unique zones characterizing extreme precipitation conditions in Nigeria. This approach allowed for the assessment of spatial and temporal patterns in extreme precipitation in the last three decades. Precipitation properties in each cluster are discussed. The cluster closest to the Atlantic has high values of precipitation intensity, frequency, and duration, whereas the cluster closest to the Sahara Desert has low values. A significant increasing trend has been observed in the frequency of rainy days at the center of the northern region of Nigeria.

Sign in / Sign up

Export Citation Format

Share Document