Identifying Temporal Patterns of Visitors to National Parks through Geotagged Photographs

Visitor data is essential for decision-making, policy formulation, and monitoring of protected areas. In this context, the data on the temporal distribution of visitors is essential to characterize influx and seasonality, and even to measure the carrying capacity of a site. However, obtaining information from visitors often involves high costs and long production times. Moreover, traditional visitor data has a limited level of detail. New sources of data can provide valuable information regarding the timing of visits. In this study, we tested the use of geotagged data to infer the temporal distribution of visitors to 15 Spanish national parks, and we identified temporal patterns of the visits at three levels: monthly, weekly, and daily. By comparing official monthly visitor counts and geotagged photographs from Flickr, we observed that the number of monthly users who upload photos significantly reflects the number of monthly visitors. Furthermore, the weekly and daily distributions of the Flickr data provided additional information that could contribute to identifying the periods of highest visitor pressure, design measures to manage the concentration of visitors, and improve the overall visitor experience. The results obtained indicate the potential of new data sources for visitor monitoring in protected areas and to open opportunities for future research. Moreover, monitoring tourism in protected areas is crucial to ensure the sustainability of their resources and to protect their biodiversity

A Study of Colloquial Place Names through Geotagged Social Media Data

Place is a rich but vague geographic concept. Much work has been done to explore the collective understanding and perceived location of place. The last few decades have seen rapid expansion in the use of online social media and data sharing services, which provide a large amount of valuable data for research of colloquial place names. This study explored how geotagged social media data can be used to understand geographic place names, and delimit the perceived geographic extent of a place. The author proposes a probabilistic method to map the perceived geographic extent of a place using Kernel Density Estimation (KDE) based on the geotagged data uploaded by users. The author also used spatio-temporal analysis methods in GIS to explore characteristics, hidden patterns, and trends of the places. Flickr, a popular online social networking service that features image hosting and sharing, was selected as the main data source for this project. The results show that outcomes of KDE with different functions and parameters differ from each other; therefore, it is crucial to select the proper KDE bandwidth in order to obtain appropriate geographic extents. Official boundaries and reference boundaries can be used to assess the geographic extents. Google Maps Street View is another useful source to examine the visual characteristics of places. Spatio-temporal analysis of the geographic extents over time reveals significant location changes of the places composed of man-made structures. Besides names and variations of place names, related colloquial terms, like Cades Cove of the Great Smoky Mountains National Park, are also useful sources when delimiting a place. Several examples are analyzed and discussed. Studies like this research can improve our understanding of geotagged Online Social Network (OSN) data in the study of colloquial place names as well as provide a temporal perspective to the analysis of their perceived geographic extents

Using VGI and Social Media Data to Understand Urban Green Space: A Narrative Literature Review

Volunteered Geographical Information (VGI) and social media can provide information about real-time perceptions, attitudes and behaviours in urban green space (UGS). This paper reviews the use of VGI and social media data in research examining UGS. The current state of the art is described through the analysis of 177 papers to (1) summarise the characteristics and usage of data from different platforms, (2) provide an overview of the research topics using such data sources, and (3) characterise the research approaches based on data pre-processing, data quality assessment and improvement, data analysis and modelling. A number of important limitations and priorities for future research are identified. The limitations include issues of data acquisition and representativeness, data quality, as well as differences across social media platforms in different study areas such as urban and rural areas. The research priorities include a focus on investigating factors related to physical activities in UGS areas, urban park use and accessibility, the use of data from multiple sources and, where appropriate, making more effective use of personal information. In addition, analysis approaches can be extended to examine the network suggested by social media posts that are shared, re-posted or reacted to and by being combined with textual, image and geographical data to extract more representative information for UGS analysis

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management

European Handbook of Crowdsourced Geographic Information

"This book focuses on the study of the remarkable new source of geographic information that has become available in the form of user-generated content accessible over the Internet through mobile and Web applications. The exploitation, integration and application of these sources, termed volunteered geographic information (VGI) or crowdsourced geographic information (CGI), offer scientists an unprecedented opportunity to conduct research on a variety of topics at multiple scales and for diversified objectives. The Handbook is organized in five parts, addressing the fundamental questions: What motivates citizens to provide such information in the public domain, and what factors govern/predict its validity?What methods might be used to validate such information? Can VGI be framed within the larger domain of sensor networks, in which inert and static sensors are replaced or combined by intelligent and mobile humans equipped with sensing devices? What limitations are imposed on VGI by differential access to broadband Internet, mobile phones, and other communication technologies, and by concerns over privacy? How do VGI and crowdsourcing enable innovation applications to benefit human society? Chapters examine how crowdsourcing techniques and methods, and the VGI phenomenon, have motivated a multidisciplinary research community to identify both fields of applications and quality criteria depending on the use of VGI. Besides harvesting tools and storage of these data, research has paid remarkable attention to these information resources, in an age when information and participation is one of the most important drivers of development. The collection opens questions and points to new research directions in addition to the findings that each of the authors demonstrates. Despite rapid progress in VGI research, this Handbook also shows that there are technical, social, political and methodological challenges that require further studies and research.

Harnessing social media data to explore urban tourist patterns and the implications for retail location modelling

The tourism landscape in urban destinations has been spatially expanded in recent years due to the increasing prevalence of sharing economy accommodation and other tourism trends. Tourists now mix with locals to form increasingly intricate population geographies within urban neighbourhoods, bringing new demand into areas which are beyond the conventional tourist locations. How these dispersed tourist demands impact local communities has become an emerging issue in both urban and tourism studies. However, progress has been hampered by the lack of fine granular travel data which can be used for understanding urban tourist patterns at the small-area level. Paying special attention to tourist grocery demand in urban destinations, the thesis takes London as the example to present the various sources of LBSN datasets that can be used as valuable supplements to conventional surveys and statistics to produce novel tourist population estimates and new tourist grocery demand layers at the small area level. First, the work examines the potential of Weibo check-in data in London for offering greater insights into the spatial travel patterns of urban tourists from China. Then, AirDNA and Twitter datasets are used in conjunction with tourism surveys and statistics in London to model the small area tourist population maps of different tourist types and generate tourist demand estimates. Finally, Foursquare datasets are utilised to inform tourist grocery travel behaviour and help to calibrate the retail location model. The tourist travel patterns extracted from various LBSN data, at both individual and collective levels, offer tremendous value to assist the construction and calibration of spatial modelling techniques. In this case, the emphasis is on improving retail location spatial Interaction Models (SIMs) within grocery retailing. These models have seen much recent work to add non-residential demand, but demand from urban tourism has yet to be included. The additional tourist demand layer generated in this thesis is incorporated into a new custom-built SIM to assess the impacts of urban tourism on the local grocery sector and support current store operations and trading potential evaluations of future investments

Using social media data to understand the urban green space use before and after a pandemic

Urban green spaces (UGSs) are essential components of urban ecosystems that provide considerable benefits to residents, including recreational opportunities, improved air and water quality, and mental and physical health benefits. The COVID-19 pandemic and related restriction measures have affected people's daily lives in numerous ways, such as remote working and learning, online shopping, social distancing, travel restrictions, and outdoor activities. During the COVID-19 pandemic, UGSs have become the main places for outdoor activities. Understanding human-environment interactions in UGSs is an important research field that has broad implications for improving policies in response to a social crisis and informing urban planning strategies. The main challenges of investigating human-environment interactions lie in effectively collecting research datasets that can reflect or reveal human behaviour patterns within UGSs. Volunteered Geographical Information (VGI) and social media can provide better information about real-time perceptions, attitudes and behaviours than traditional datasets such as surveys and questionnaires. This provides great opportunities to investigate human-environment interactions in UGS in real-time. Additionally, Twitter is one of the most popular social networks, and it can provide more comprehensive and unbiased datasets through a new academic research Application Programming Interface (API). The overall aim of this thesis is to evaluate the contributions of UGS to human well-being, during a time of crisis, by investigating the characteristics and spatial-temporal patterns of UGS use across three periods: pre-, during- and after the COVID-19 pandemic. The thesis will document the process of examining spatial-temporal changes in UGS use associated with COVID-19 related pandemic, by using Twitter datasets incorporating approaches including text mining, topic modelling and spatial-temporal analysis. This is the first study to examine social media data over consistent time period before, during and after the lockdown in relation to UGS. The results show that the findings and method can potentially inform policy makers in their management and planning of UGS, especially in a period of social crisis like the COVID-19 pandemic. This research has great potential to help improve urban green space planning and management in urban areas

Social media and GIScience: Collection, analysis, and visualization of user-generated spatial data

Over the last decade, social media platforms have eclipsed the height of popular culture and communication technology, which, in combination with widespread access to GIS-enabled hardware (i.e. mobile phones), has resulted in the continuous creation of massive amounts of user-generated spatial data. This thesis explores how social media data have been utilized in GIS research and provides a commentary on the impacts of this next iteration of technological change with respect to GIScience. First, the roots of GIS technology are traced to set the stage for the examination of social media as a technological catalyst for change in GIScience. Next, a scoping review is conducted to gather and synthesize a summary of methods used to collect, analyze, and visualize this data. Finally, a case study exploring the spatio-temporality of crowdfunding behaviours in Canada during the COVID-19 pandemic is presented to demonstrate the utility of social media data in spatial research