Spatiotemporal Analysis of Tourists and Residents in Shanghai Based on Location-Based Social Network’s Data from Weibo

The aim of this study is to analyze and compare the patterns of behavior of tourists and residents from Location-Based Social Network (LBSN) data in Shanghai, China using various spatiotemporal analysis techniques at different venue categories. The paper presents the applications of location-based social network’s data by exploring the patterns in check-ins over a period of six months. We acquired the geo-location information from one of the most famous Chinese microblogs called Sina-Weibo (Weibo). The extracted data is translated into the Geographical Information Systems (GIS) format, and compared with the help of temporal statistical analysis and kernel density estimation. The venue classification is done by using information regarding the nature of physical locations. The findings reveal that the spatial activities of tourists are more concentrated as compared to those of residents, particularly in downtown, while the residents also visited suburban areas and the temporal activities of tourists varied significantly while the residents’ activities showed relatively stable behavior. These results can be applied in destination management, urban planning, and smart city development.

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Spatiotemporal Analysis of Residents in Shanghai by Utilizing Chinese Microblog Weibo Data

Mobile Information Systems ◽

10.1155/2021/8396771 ◽

2021 ◽

Vol 2021 ◽

pp. 1-10

Author(s):

Li Hou ◽

Qi Liu ◽

Mueen Uddin ◽

Hizbullah Khattak ◽

Muhammad Asshad

Keyword(s):

Geographical Information Systems ◽

Spatiotemporal Analysis ◽

Geographical Information ◽

Time Frequency ◽

Data Assessment ◽

Usage Patterns ◽

Location Based Social Network ◽

And Gender ◽

Visualization Techniques ◽

The Impact

Mobile applications are really important nowadays due to providing the accurate check-in data for research. The primary goal of the study is to look into the impact of several forms of entertainment activities on the density dispersal of occupants in Shanghai, China, as well as prototypical check-in data from a location-based social network using a combination of temporal, spatial, and visualization techniques and categories of visitors’ check-ins. This article explores Weibo for big data assessment and its reliability in a variety of categories rather than physically obtained information by examining the link between time, frequency, place, class, and place of check-in based on geographic attributes and related implications. The data for this study came from Weibo, a popular Chinese microblog. It was preprocessed to extract the most important and associated results elements, then converted to geographical information systems format, appraised, and finally displayed using graphs, tables, and heat maps. For data significance, a linear regression model was used, and, for spatial analysis, kernel density estimation was utilized. As per results of hours-to-day usage patterns, enjoyment activities and frequency distribution are produced. Our findings are based on the check-in behaviour of users at amusement locations, the density of check-ins, rush periods for visiting amusement locations, and gender differences. Our data provide light on different elements of human behaviour patterns, the importance of entertainment venues, and their impact in Shanghai. So it can be used in pattern recognition, endorsement structures, and additional multimedia content for these collections.

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Identification of submerged large woody debris from single-beam echo soundings

Journal of Hydroinformatics ◽

10.2166/jh.2006.023 ◽

2006 ◽

Vol 8 (1) ◽

pp. 1-12 ◽

Cited By ~ 3

Author(s):

Laurent White ◽

Ben R. Hodges ◽

Barney N. Austin ◽

Tim D. Osting

Keyword(s):

Geographical Information Systems ◽

Woody Debris ◽

Scale Space ◽

Large Woody Debris ◽

Geographical Information ◽

Triangulated Irregular Network ◽

Single Beam ◽

Analysis Techniques ◽

Scale Space Analysis ◽

Discrimination Method

Submerged large woody debris (LWD) in rivers and streams appears as spikes in bathymetry data collected at a decimeter resolution with a single-beam echo sounder. The LWD signal distorts any subsequent interpolation of bathymetry to an hydraulic model mesh or to a triangulated irregular network (TIN) for Geographical Information Systems (GIS). Two methods for separating the submerged LWD from the background bathymetry are investigated: 1) a new σ-discrimination method and 2) an adaptation of prior scale-space analysis techniques. The former is shown to effectively separate LWD from the background bathymetry. However, the latter is shown to be ineffective for this purpose. Separation of the background bathymetry and LWD signal allows the quantity and distribution of LWD to be separately mapped, providing a resource for biologists, geomorphologists and hydraulic engineers whose studies may be affected by the presence of LWD.

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COMBINING GEOSPATIAL ANALYSIS WITH DEMENTIA RISK UTILISING GENERAL PRACTICE DATA: A SYSTEMATIC REVIEW

Journal of Prevention of Alzheimer's Disease ◽

10.14283/jpad.2017.33 ◽

2017 ◽

pp. 1-7

Author(s):

N. Bagheri ◽

K. Wangdi ◽

N. Cherbuin ◽

K.J. Anstey

Keyword(s):

Risk Assessment ◽

General Practice ◽

Information Systems ◽

Geographical Information Systems ◽

Geospatial Analysis ◽

Assessment Tools ◽

Geographical Information ◽

Analysis Techniques ◽

Dementia Risk ◽

Risk Assessment Tools

Geographical information systems (GIS) and geospatial analysis techniques will help to identify significant dementia risk clusters (hotspots) across communities and will enable policy makers to target prevention interventions to the right place. This review synthesises the published literature on geospatial analysis techniques for quantifying and mapping dementia risk, and reviews available dementia risk assessment tools. A systematic literature review was undertaken in four medical and life sciences databases (PubMed, Cochrane Central, Embase, and Web of Sciences) from their inception to March 2017 for all articles relating to dementia. The search terms included: ‘dementia’, ‘Alzheimer’s disease’, ‘general practice database’, ‘family physician’, ‘AD risk assessment tools’, ‘Geographical Information Systems’ and ‘geospatial analysis’, ‘geographical variation’ and ‘spatial variation’. To date, most geospatial studies on dementia have been carried out retrospectively using population based data. An alternative approach is utilisation of a rich source of general practice (family physician) databases to predict dementia risk based on available dementia risk assessment tools. In conclusion, the estimated risks of dementia can thus be geo-attributed and mapped at a small scale using geographical information systems and geospatial analysis techniques to identify dementia risk clusters across the communities and refine our understanding of the interaction between socio-demographic and environmental factors, and dementia risk clusters.

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Big Data Analysis to Observe Check-in Behavior Using Location-Based Social Media Data

Information ◽

10.3390/info9100257 ◽

2018 ◽

Vol 9 (10) ◽

pp. 257 ◽

Cited By ~ 6

Author(s):

Muhammad Rizwan ◽

Wanggen Wan

Keyword(s):

Social Media ◽

Kernel Density ◽

Big Data Analysis ◽

Location Based Services ◽

Social Media Data ◽

Sina Weibo ◽

Density Surface ◽

Location Based Social Network ◽

Gender Based ◽

Media Data

With rapid advancement in location-based services (LBS), their acquisition has become a powerful tool to link people with similar interests across long distances, as well as connecting family and friends. To observe human behavior towards using social media, it is essential to understand and measure the check-in behavior towards a location-based social network (LBSN). This check-in phenomenon of sharing location, activities, and time by users has encouraged this research on the frequency of using an LBSN. In this paper, we investigate the check-in behavior of several million individuals, for whom we observe the gender and their frequency of using Chinese microblog Sina Weibo (referred as “Weibo”) over a period in Shanghai, China. To produce a smooth density surface of check-ins, we analyze the overall spatial patterns by using the kernel density estimation (KDE) by using ArcGIS. Furthermore, our results reveal that female users are more inclined towards using social media, and a difference in check-in behavior during weekday and weekend is also observed. From the results, LBSN data seems to be a complement to traditional methods (i.e., survey, census) and is used to study gender-based check-in behavior.

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Spatiotemporal Clustering and Analysis of Road Accident Hotspots by Exploiting GIS Technology and Kernel Density Estimation

The Computer Journal ◽

10.1093/comjnl/bxz158 ◽

2020 ◽

Author(s):

Syed Saqib Ali Kazmi ◽

Mehreen Ahmed ◽

Rafia Mumtaz ◽

Zahid Anwar

Keyword(s):

Density Estimation ◽

Kernel Density Estimation ◽

Geographical Information Systems ◽

Traffic Accidents ◽

Road Traffic ◽

Transportation Network ◽

Kernel Density ◽

Road Accident ◽

Geographical Information ◽

Gis Technology

Abstract Traffic accidents are a common problem in any transportation network. Road traffic accidents are predicted to be the seventh leading cause of deaths by the year 2030. Recently research in the integration of geographical information systems (GIS) for analyzing accidents, road design and safety management has increased considerably. The perpetual use of GIS tools, lead this study to propose the identification of accident hotspots by exploiting GIS technology coupled with kernel density estimation (KDE). This paper proposes the use of KDE technique and GIS technology to automatically identify the accident hotspots using UK as the study area. Analysis shows that most of the accidents occur when there is a 30 mph speed limit, a weekend, in the evening time, during the months of October and November, on the single carriageway, where there is ‘T’ or staggered junction and on ‘A’ road class. Moreover, this study also proposed techniques to classify the accident severity that is classified as either fatal, serious or slight. The driver behavior and environmental features achieved an accuracy up to 85% on the severity classification with Bagging technique. Further, the shortcomings, limitations and recommendations for future work are also identified.

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Analyzing the Check-In Behavior of Visitors through Machine Learning Model by Mining Social Network’s Big Data

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6323357 ◽

2021 ◽

Vol 2021 ◽

pp. 1-11

Author(s):

Li Hou ◽

Qi Liu ◽

Jamel Nebhen ◽

Mueen Uddin ◽

Mujahid Ullah ◽

...

Keyword(s):

Machine Learning ◽

Statistical Analysis ◽

Kernel Density ◽

Sina Weibo ◽

Spatial Statistical Analysis ◽

Machine Learning Model ◽

Current Article ◽

Location Based Social Network ◽

Outlying Regions ◽

Health Purpose

The current article paper is aimed at assessing and comparing the seasonal check-in behavior of individuals in Shanghai, China, using location-based social network (LBSN) data and a variety of spatiotemporal analytic techniques. The article demonstrates the uses of location-based social network’s data by analyzing the trends in check-ins throughout a three-year term for health purpose. We obtained the geolocation data from Sina Weibo, one of the biggest renowned Chinese microblogs (Weibo). The composed data is converted to geographic information system (GIS) type and assessed using temporal statistical analysis and spatial statistical analysis using kernel density estimation (KDE) assessment. We have applied various algorithms and trained machine learning models and finally satisfied with sequential model results because the accuracy we got was leading amongst others. The location cataloguing is accomplished via the use of facts about the characteristics of physical places. The findings demonstrate that visitors’ spatial operations are more intense than residents’ spatial operations, notably in downtown. However, locals also visited outlying regions, and tourists’ temporal behaviors vary significantly while citizens’ movements exhibit a more steady stable behavior. These findings may be used in destination management, metro planning, and the creation of digital cities.

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Accident Hotspot Identification on the Midnapore Kharagpur Development Authority Planning Area.

International Journal of Recent Technology and Engineering - 2 ◽

10.35940/ijrte.b3351.079220 ◽

2020 ◽

Vol 9 (2) ◽

pp. 169-174 ◽

Cited By ~ 1

Keyword(s):

Geographical Information Systems ◽

Black Spot ◽

Kernel Density ◽

Road Accident ◽

Geographical Information ◽

Road Accidents ◽

Police Station ◽

Black Spots ◽

The World ◽

Accident Data

Road accidents are a vital problem in our country for various reasons. According to WHO reports, approximately 1.25 million people died each year, and more than 50 million people injured in road accidents all over the world. Road accident is mostly human-made, and it's affecting your life negatively. Regarding, many studies or research has been performed to reduce road accident and identify the accident blackspot. This paper represents a methodology to find out the accident-prone zone, estimation of Kernel Density and black Site & black Spot identification of major roads Medinipur and Kharagpur development Authority (MKDA) planning area using of Geographical Information Systems (GIS). For this study, road accident data collected from Paschim Medinipur Kotwali Police station from 2016 to 2019. A kernel density estimation was created to identify black spots & black sites of the study area. Based on the result, suggestions are provided to improve the situation in the future.

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A Study of User Activity Patterns and the Effect of Venue Types on City Dynamics Using Location-Based Social Network Data

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9120733 ◽

2020 ◽

Vol 9 (12) ◽

pp. 733

Author(s):

Naimat Ullah Khan ◽

Wanggen Wan ◽

Shui Yu ◽

A. A. M. Muzahid ◽

Sajid Khan ◽

...

Keyword(s):

Social Network ◽

Human Behavior ◽

Geographical Information Systems ◽

Activity Patterns ◽

Geographical Information ◽

Time Frequency ◽

City Dynamics ◽

Location Based Social Network ◽

Spatio Temporal ◽

The City

The main purpose of this research is to study the effect of various types of venues on the density distribution of residents and model check-in data from a Location-Based Social Network for the city of Shanghai, China by using combination of multiple temporal, spatial and visualization techniques by classifying users’ check-ins into different venue categories. This article investigates the use of Weibo for big data analysis and its efficiency in various categories instead of manually collected datasets, by exploring the relation between time, frequency, place and category of check-in based on location characteristics and their contributions. The data used in this research was acquired from a famous Chinese microblogs called Weibo, which was preprocessed to get the most significant and relevant attributes for the current study and transformed into Geographical Information Systems format, analyzed and, finally, presented with the help of graphs, tables and heat maps. The Kernel Density Estimation was used for spatial analysis. The venue categorization was based on nature of the physical locations within the city by comparing the name of venue extracted from Weibo dataset with the function such as education for schools or shopping for malls and so on. The results of usage patterns from hours to days, venue categories and frequency distribution into these categories as well as the density of check-in within the Shanghai and contribution of each venue category in its diversity are thoroughly demonstrated, uncovering interesting spatio-temporal patterns including frequency and density of users from different venues at different time intervals, and significance of using geo-data from Weibo to study human behavior in variety of studies like education, tourism and city dynamics based on location-based social networks. Our findings uncover various aspects of activity patterns in human behavior, the significance of venue classes and its effects in Shanghai, which can be applied in pattern analysis, recommendation systems and other interactive applications for these classes.

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