Visualization, Spatiotemporal Patterns, and Directional Analysis of Urban Activities Using Geolocation Data Extracted from LBSN

Understanding Public Perspectives on Fracking in the United States using Social Media Big Data

People's attitudes toward hydraulic fracturing (i.e., "fracking") to extract fossil fuels can be shaped by factors associated with socio-demographics, economic development, social equity and politics, environmental impacts, and fracking-related information obtainment. Existing research typically conducts surveys and interviews to study public attitudes toward fracking among a small group of individuals in a specific geographic area, where limited samples may introduce bias. Here, we compiled geo-referenced social media big data from Twitter during 2018-2019 for the entire United States to present a more holistic picture of people's attitudes toward fracking. We used a multiscale geographically weighted regression (MGWR) to investigate county-level relationships between the aforementioned factors and percentages of negative tweets concerning fracking. Results clearly depict spatial heterogeneity and varying scales of those associations. Counties with higher median household income, larger African American populations, and/or lower educational level are less likely to oppose fracking, and these associations show global stationarity in all contiguous U.S. counties. Eastern and Central U.S. counties with higher unemployment rate, counties east of the Great Plains with less fracking sites nearby, and Western and Gulf Coast region counties with higher health insurance enrollments are more likely to oppose fracking activities. These three variables show clear East-West geographical divides in influencing public perspective on fracking. In counties across the southern Great Plains, negative attitudes toward fracking are less often vocalized on Twitter as the share of Republican voters increases. These findings have implications for both predicting public perspectives and needed policy adjustments. The methodology can also be conveniently applied to investigate public perspectives on other controversial topics.

Transformation and Sustainable Development of Shopping Centers: Case of Czech and Slovak Cities

The aim of the paper is to critically evaluate the similarities and differences in the development of the temporal and spatial structure of shopping centers in the Czech and Slovak republics. We focused on the retail transformation and sustainable manifestations of the location and construction of shopping centers. We classified shopping centers according to their genesis, location in the city, and size of the gross leasable area. To analyze migration trends and geographic distribution characteristics of shopping centers in the capital cities of both countries (local level of analysis), we used spatial gravity and standard deviational ellipse. Generally, there is an analogous trend in the development of shopping centers in the Czech Republic and Slovakia, with a particular two- to four-year lag in Slovakia (west–east gradient). Despite this, we still perceive the demand for shopping centers in both countries as above average, and it is not declining. The construction of shopping centers, mainly in small towns, also indicates this trend. In Prague and Bratislava, the pattern of spatial expansion of shopping centers differs. Prague probably represents a more advanced phase of shopping center agglomeration. However, neither country has reached the state of clustering.

Measurement of Potential Victims of Burglary at the Mesoscale: Comparison of Census, Phone Users, and Social Media Data

Since the target of burglars is generally the property of the inhabitant, it is crucial to accurately measure potential victims when analyzing burglaries, especially in small areas. Previous studies on burglary are mostly based on large units such as census tracts or communities. One of the difficulties is the measurement of the potential victims of burglary at the mesoscale. We compare the measuring effects of census population, census households, nighttime mobile phone users, and nighttime social media, such as the Tencent regional heatmap (TRH), on potential victims of burglary on 150 m × 150 m grids. Based on the rational choice theory, and controlling for the potentially confounding effects of risks and cost, we show that the TRH performed best, followed by census households and census population, and phone users performed poorly. The best-performing time period for TRH data was 3:00–5:00 am on weekends. These findings could lead to an improved measurement of potential victims of burglary at the mesoscale, and could provide scientific insight for crime prevention.

Spatiotemporal data mining: a survey on challenges and open problems

Spatiotemporal data mining (STDM) discovers useful patterns from the dynamic interplay between space and time. Several available surveys capture STDM advances and report a wealth of important progress in this field. However, STDM challenges and problems are not thoroughly discussed and presented in articles of their own. We attempt to fill this gap by providing a comprehensive literature survey on state-of-the-art advances in STDM. We describe the challenging issues and their causes and open gaps of multiple STDM directions and aspects. Specifically, we investigate the challenging issues in regards to spatiotemporal relationships, interdisciplinarity, discretisation, and data characteristics. Moreover, we discuss the limitations in the literature and open research problems related to spatiotemporal data representations, modelling and visualisation, and comprehensiveness of approaches. We explain issues related to STDM tasks of classification, clustering, hotspot detection, association and pattern mining, outlier detection, visualisation, visual analytics, and computer vision tasks. We also highlight STDM issues related to multiple applications including crime and public safety, traffic and transportation, earth and environment monitoring, epidemiology, social media, and Internet of Things.

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.