4D+SNN: A Spatio-Temporal Density-Based Clustering Approach with 4D Similarity

2013 ◽  
Author(s):  
Ricardo Oliveira ◽  
Maribel Yasmina Santos ◽  
Joao Moura Pires
Keyword(s):  
Density Based Clustering ◽  
Clustering Approach ◽  
Spatio Temporal

scholarly journals A spatio-temporal active-fire clustering approach for global burned area mapping at 250 m from MODIS data

2020 ◽  
Vol 236 ◽  
pp. 111493 ◽  
Author(s):  
Joshua Lizundia-Loiola ◽  
Gonzalo Otón ◽  
Rubén Ramo ◽  
Emilio Chuvieco
Keyword(s):  
Burned Area ◽  
Modis Data ◽  
Active Fire ◽  
Burned Area Mapping ◽  
Clustering Approach ◽  
Spatio Temporal

scholarly journals Unsupervised Hierarchical Clustering Approach for Tourism Market Segmentation Based on Crowdsourced Mobile Phone Data

Sensors ◽  
2018 ◽  
Vol 18 (9) ◽  
pp. 2972 ◽  
Author(s):  
Jorge Rodríguez ◽  
Ivana Semanjski ◽  
Sidharta Gautama ◽  
Nico Van de Weghe ◽  
Daniel Ochoa
Keyword(s):  
Hierarchical Clustering ◽  
Market Segmentation ◽  
Wearable Sensors ◽  
Essential Element ◽  
Cost Effective ◽  
Mobile Phone Data ◽  
Mobility Data ◽  
Tourism Management ◽  
Clustering Approach ◽  
Spatio Temporal

Understanding tourism related behavior and traveling patterns is an essential element of transportation system planning and tourism management at tourism destinations. Traditionally, tourism market segmentation is conducted to recognize tourist’s profiles for which personalized services can be provided. Today, the availability of wearable sensors, such as smartphones, holds the potential to tackle data collection problems of paper-based surveys and deliver relevant mobility data in a timely and cost-effective way. In this paper, we develop and implement a hierarchical clustering approach for smartphone geo-localized data to detect meaningful tourism related market segments. For these segments, we provide detailed insights into their characteristics and related mobility behavior. The applicability of the proposed approach is demonstrated on a use case in the Province of Zeeland in the Netherlands. We collected data from 1505 users during five months using the Zeeland app. The proposed approach resulted in two major clusters and four sub-clusters which we were able to interpret based on their spatio-temporal patterns and the recurrence of their visiting patterns to the region.

Towards retrieving social events from spatio-temporal photo annotation

First Monday ◽  
2019 ◽  
Author(s):  
Davi Oliveira Serrano De Andrade ◽  
Anderson Almeida Firmino ◽  
Cláudio de Souza Baptista ◽  
Hugo Feitosa De Figueirêdo
Keyword(s):  
Clustering Algorithm ◽  
Geographical Location ◽  
Spatial Clusters ◽  
Public Database ◽  
Common Goal ◽  
Social Events ◽  
Clustering Approach ◽  
Spatio Temporal ◽  
Event Annotation ◽  
Photo Annotation

An event can be defined as a happening that gathers people with some common goal over a period of time and in a certain place. This paper presents a new method to retrieve social events through annotations in spatio-temporal photo collections, known as STEve-PR (Spatio-Temporal EVEnt Photo Retrieval). The proposed technique uses a clustering algorithm to gather similar photos by considering the location, date and time of the photos. The STEve-PR clustering approach clusters photos belonging to the same event. STEve-PR uses spatial clusters created to propagate event annotation between photos in the same cluster and employs TF-IDF similarity between tags to find the spatial cluster with the highest similarity for photos without a geographical location. We evaluated our approach on a public database.

scholarly journals Proposing a Density-Based Clustering Approach (DBCA) to Aggregate Data Collected from the Environment in Arid Area for Desertification

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Zhihao Peng ◽  
Raziyeh Daraei ◽  
Seyed Mojtaba Ahmadpanahi ◽  
Amir Seyed Danesh ◽  
Safieh Siadat ◽  
...  
Keyword(s):  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Arid Area ◽  
Sensor Nodes ◽  
Base Stations ◽  
Wireless Sensor ◽  
Arid Areas ◽  
Density Based Clustering ◽  
Energy Limitation ◽  
Clustering Approach

Nowadays, the expansion of desert areas has become one of the main problems in arid areas due to various reasons such as rising temperatures and vegetation fires. Establishment of wireless sensor networks in these areas can accelerate the process of environmental monitoring and integrate temperature and humidity information sending to base stations in order to make basic decisions on desertification. The main problem in this regard is the energy limitation of sensor nodes in wireless sensor networks, which is one of the main challenges in using these nodes due to the lack of a fixed power supply. Because the node consumes the most energy during data transmission, the node that transmits the most data or sends the packets over long distances runs out of energy faster than the others and the network work process is disrupted. Therefore, in this study, a density-based clustering approach is proposed to integrate data collected from the environment in arid areas for desertification. In the proposed method at each step, the node that has the most residual energy and is highly centralized will be selected to transfer information. The results of experiments for evaluating the performance of the proposed method show that the proposed method balances the energy consumption of the nodes and optimizes the lifespan of the nodes in the wireless sensor network installed in the arid area.

scholarly journals Indexing Multivariate Mobile Data through Spatio-Temporal Event Detection and Clustering

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 448 ◽  
Author(s):  
Reza Rawassizadeh ◽  
Chelsea Dobbins ◽  
Mohammad Akbari ◽  
Michael Pazzani
Keyword(s):  
Event Detection ◽  
Clustering Algorithm ◽  
Search Space ◽  
Detection Algorithm ◽  
Sensor Data ◽  
Data Types ◽  
Contextual Data ◽  
User Behaviors ◽  
Clustering Approach ◽  
Spatio Temporal

Mobile and wearable devices are capable of quantifying user behaviors based on their contextual sensor data. However, few indexing and annotation mechanisms are available, due to difficulties inherent in raw multivariate data types and the relative sparsity of sensor data. These issues have slowed the development of higher level human-centric searching and querying mechanisms. Here, we propose a pipeline of three algorithms. First, we introduce a spatio-temporal event detection algorithm. Then, we introduce a clustering algorithm based on mobile contextual data. Our spatio-temporal clustering approach can be used as an annotation on raw sensor data. It improves information retrieval by reducing the search space and is based on searching only the related clusters. To further improve behavior quantification, the third algorithm identifies contrasting events withina cluster content. Two large real-world smartphone datasets have been used to evaluate our algorithms and demonstrate the utility and resource efficiency of our approach to search.

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