Analyzing Social-Geographic Human Mobility Patterns Using Large-Scale Social Media Data

Population Space–Time Patterns Analysis and Anthropic Pressure Assessment of the Insubric Lakes Using User-Generated Geodata

Human activities are one of the main causes of lake-water pollution and eutrophication. The study of human pressure around lakes is of importance to understand its effects on the lakes natural resources. Social media data is a valuable space–time-resolved information source to detect human dynamics. In this study, user-generated geodata, namely users’ location records provided by the Facebook Data for Good program, are used to assess population patterns and infer the magnitude of anthropic pressure in the areas surrounding the Insubric lakes (Maggiore, Como and Lugano) between Northern Italy and Southern Switzerland. Patterns were investigated across different lakes’ neighbouring areas as well as seasons, days of the week, and day hours in the study period May 2020–August 2021. Two indicators were conceived, computed and mapped to assess the space–time distribution of users around lakes and infer the anthropic pressure. The highest pressure was found around lakes Maggiore and Como coastal areas during weekends in summer (up to +14% average users presence than weekdays in winter), suggesting tourism is the primary accountable reason for the pressure. Contrarily, around lake Lugano, the population dynamic is mostly affected by commuters or weekly workers, where the maximum pressure occurs during weekdays in all seasons (+6.6% average users presence than weekends). Results provide valuable input to further analyses connected, for example, to the correlation between human activities and lake-water quality and/or prediction models for anthropic pressure and tourism fluxes on lakes that are foreseen for the future development of this work.

GIS-Based Approach for the Analysis of Geographical Education Paths

This paper presents a new geospatial approach, and a proposal to study the geographic educational path of individuals or social groups identified by researchers using a Geographic Information System (GIS) and spatial statistics. A scheme of research proceedings has been proposed, including obtaining data from various sources (including surveys and other sources, e.g., from the university and OpenStreetMap), their proper preparation and categorisation into one geodatabase on the GIS system, followed by visualisation and the calculation of statistics. The whole research procedure was carried out in GIS. The results can be useful for detecting patterns of educational paths in different countries and social groups, and comparing them. Indirectly, they can be used to study mobility, and to indicate the spatial range of studied schools. The study was carried out among a group of students of geoinformation at the University of Lodz. Visualization and analysis of their geographical educational path showed that most of them attended schools close to where they lived, indicating low mobility during their education. The results obtained may be relevant to the “spatial turn” in education research.

Analysis of Green Spaces by Utilizing Big Data to Support Smart Cities and Environment: A Case Study About the City Center of Shanghai

Green areas or parks are the best way to encourage people to take part in physical exercise. Traditional techniques of researching the attractiveness of green parks, such as surveys and questionnaires, are naturally time consuming and expensive, with less transferable outcomes and only site-specific findings. This research provides a factfinding study by means of location-based social network (LBSN) data to gather spatial and temporal patterns of green park visits in the city center of Shanghai, China. During the period from July 2014 to June 2017, we examined the spatiotemporal behavior of visitors in 71 green parks in Shanghai. We conducted an empirical investigation through kernel density estimation (KDE) and relative difference methods on the effects of green spaces on public behavior in Shanghai, and our main categories of findings are as follows: (i) check-in distribution of visitors in different green spaces, (ii) users’ transition based on the hours of a day, (iii) famous parks in the study area based upon the number of check-ins, and (iv) gender difference among green park visitors. Furthermore, the purpose of obtaining these outcomes can be utilized in urban planning of a smart city for green environment according to the preferences of visitors.

Role of Big Data in the Development of Smart City by Analyzing the Density of Residents in Shanghai

In recent decades, a large amount of research has been carried out to analyze location-based social network data to highlight their application. These location-based social network datasets can be used to propose models and techniques that can analyze and reproduce the spatiotemporal structures and symmetries in user activities as well as density estimations. In the current study, different density estimation techniques are utilized to analyze the check-in frequency of users in more detail from location-based social network dataset acquired from Sina-Weibo, also referred as Weibo, over a specific period in 10 different districts of Shanghai, China. The aim of this study is to analyze the density of users in Shanghai city from geolocation data of Weibo as well as to compare their density through univariate and bivariate density estimation techniques; i.e., point density and kernel density estimation (KDE) respectively. The main findings of the study include the following: (i) characteristics of users’ spatial behavior, the center of activity based on their check-ins, (ii) the feasibility of check-in data to explain the relationship between users and social media, and (iii) the presentation of evident results for regulatory or managing authorities for urban planning. The current study shows that the point density and kernel density estimation. KDE methods provide useful insights for modeling spatial patterns using geo-spatial dataset. Finally, we can conclude that, by utilizing the KDE technique, we can examine the check-in behavior in more detail for an individual as well as broader patterns in the population as a whole for the development of smart city. The purpose of this article is to figure out the denser places so that the authorities can divide the mobility of people from the same routes or at least they can control the situation from any further inconvenience.