Winescape perception and big data analysis: an assessment through social media photographs in the Chianti Classico region

Quantifying and mapping the relevant landscape attributes of winescape is difficult due to both the complex identity characterization of the places and the multidimensionality of the pursued perceptive experience on the emotional level. Although the quality of the rural landscape is recognized as an essential element of winescape, in the literature there are no methodological and applicative studies on the identification of the most significant characteristics of a wine region that are fundamental attributes in the preferences of visitors. The aim of the work is to propose a methodology to link the environmental and cultural landscape characteristics of the territory with the concept of winescape to improve the image of wine tourism adopting a systematic approach for territorial branding starting from the analysis of the visitors' preferences. The analysis is conducted through the geographical information data shared on the social media Flickr. Different methods of analysis are applied in an integrated way to: a) analyze the demand for winescape in its different dimensions; b) identify the territorial variables that are part of the winescape supply; c) build a spatial relationship model between winescape demand and supply to quantify the territorial suitability and provide useful information for rural development strategies. Keywords: Winescape, Big data, Landscape quality, Image clustering, Maxent, Wine tourism: territorial marketin

Assessing the perception of urban visual quality: an approach integrating big data and geostatistical techniques

Human well-being is affected by the design quality of the city in which they live and walk. This depends primarily on specific physical characteristics and how they are aggregated together. Many studies have highlighted the great potential of photographic data shared on the Flickr platform for analyzing environmental perceptions in landscape and urban planning. Other researchers have used panoramic images from the Google Street View (GSV) web service to extract data on urban quality. However, at the urban level, there are no studies correlating quality perceptions detected by social media platforms with spatial geographic characteristics through geostatistical models. This work proposes the analysis of urban quality in different areas of the Livorno city through a methodological approach based on Geographical Random Forest regression. The result offers important insights into the physical characteristics of a street environment that contribute to the more abstract qualities of urban design

The use of crowdsourced geographic information for spatial evaluation of cultural ecosystem services in the agricultural landscape: the case of Chianti Classico (Italy)

The use of geo-tagged photographs seems to be a promising alternative to assess Cultural Ecosystem Services CESs in respect to the traditional investigation when focusing on the study of the aesthetic appreciation of a protected area or natural landscape. The aim of this study is integrating the cumulative viewshed calculated from geotagged photo metadata publicly shared on Flickr with raster data on infrastructure, historical sites, and the natural environment, using landscape ecology metrics and RandomForest modelling. Crowdsourced data provided empirical assessments of the covariates associated with visitor distribution, highlighting how changes in infrastructure, crops and environmental factors can affect visitor's use. These data can help researchers, managers, and public planners to develop projects, and guidelines in the rural landscape for incresing the supply for CESs

Urban landscape assessment: a perceptual approach combining virtual reality and crowdsourced photo geodata

The paper focuses on the evaluation of the visual quality of the cityscape of Livorno, in Italy. Using an algorithm based on Flickr’s Application Programming Interface, the coordinates of 7,453 shooting points of a shared photo were downloaded. A direct survey was conducted to assess the perception of urban landscape. The geographical spaces to be evaluated were measured using 78 spherical images played in virtual reality through a virtual reality headset.The data were processed with the Principal Components Analysis. A Visual Quality Index (VQI) was developed to spatialise the results. Saliency maps were calculated to survey typical patterns in areas with high VQI. The results demonstrate how subjective visual perception helps identifying the criteria for the design of high-quality public spaces

Integrating Remote Sensing and Street View Images to Quantify Urban Forest Ecosystem Services

There is an urgent need for holistic tools to assess the health impacts of climate change mitigation and adaptation policies relating to increasing public green spaces. Urban vegetation provides numerous ecosystem services on a local scale and is therefore a potential adaptation strategy that can be used in an era of global warming to offset the increasing impacts of human activity on urban environments. In this study, we propose a set of urban green ecological metrics that can be used to evaluate urban green ecosystem services. The metrics were derived from two complementary surveys: a traditional remote sensing survey of multispectral images and Laser Imaging Detection and Ranging (LiDAR) data, and a survey using proximate sensing through images made available by the Google Street View database. In accordance with previous studies, two classes of metrics were calculated: greenery at lower and higher elevations than building facades. In the last phase of the work, the metrics were applied to city blocks, and a spatially constrained clustering methodology was employed. Homogeneous areas were identified in relation to the urban greenery characteristics. The proposed methodology represents the development of a geographic information system that can be used by public administrators and urban green designers to create and maintain urban public forests

Impact of Climate Change on Wine Tourism: An Approach through Social Media Data

Wine tourism is one of the best opportunities for rural development, but because it is partially exposed to climatic conditions, it is a climate-vulnerable tourism activity. However, an understanding of the potential impacts of global climate change on this popular activity remains limited. This study proposes a new methodology that combines current daily gridded climate data from the E-OBS project with big spatiotemporal data from the Flickr photo-sharing platform through a generalized additive model This methodology was implemented to study the potential impacts on tourism flows due to climate change and to make predictions about the future using data from the CMIP5 project. We applied the methodology to 5 European wine tourism regions: Alsace (FR), Chianti (IT), La Rioja (SP), Langhe-Monferrato (IT), and Moselle (DE). Results show an increased probability of presence and increased deseasonalization of tourism in all study areas and an anticipation of peak presence from summer to spring in three of the five regions. We believe that these results can be useful for public and private stakeholders to adapt the offer of wine tourism services to changes in demand and to direct the organization of events such as festivals and thematic tours

Exploring National Park Visitors’ Judgements from Social Media: The Case Study of Plitvice Lakes National Park

This study aims to conduct a survey of visitor reviews of the Plitvice Lakes National Park in Croatia to detect strengths and weaknesses of the park. In total, 15,673 reviews written in the period between 2007 and 2021 were scraped from the social media platform TripAdvisor. The research applies a comprehensive combination of multidimensional scaling, sentiment analysis, and natural language processing approaches to a sample area of international naturalistic interest. Analyzing the opinions of visitors, the authors identify: the main topics of interest related to the management of the park; and the strengths and weaknesses on the basis of definitely positive and decidedly negative reviews, respectively. The tested methodology is easily applicable for the analysis of different naturalistic contexts and protected areas, even in different countries, thanks to the use of translated reviews. The results obtained show that visitors to protected natural areas are not only interested in naturalistic and landscape aspects but also in issues such as accessibility and management of routes and visits