Geographical index of concentration as an indicator of the spatial distribution of tourist attractions in Belgrade

The spatial structure of tourist attractions can be presented both qualitatively and quantitatively. One of the indicators of the spatial structure of tourism is the index of geographical concentration of tourist attractions. The geographical concentration of tourist attractions represents the ratio of the number of tourist attractions in the observed area and its structural parts and the total number of structural units of the analyzed area. This paper aims to determine the spatial distribution of attractions in the administrative territories of Belgrade municipalities and to establish correlations with tourist attendance. The number and spatial distribution of accommodation capacities are the largest in the central city municipalities so that the number of visitors is the largest in them. At the same time, the central city municipalities have the highest concentration of tourist attractions. For data collection, the authors used field research, OSM (Open Street Maps), Google maps, with software processing ArcGIS 10.2. The research results enabled the definition of the model of distribution of tourist attractions and indicated its application. This model of distribution of tourist attractions shows that they are mostly concentrated in the city center. This also means a small spatial connection of tourist attractions in the city center and peripheral parts

Using Social Network Data to Improve Planning and Design of Smart Cities

The Smart City concept has transformed the meaning of citizen participation. Smart Cities are characterized by being inclusive cities, i.e., cities for all. But the concept of inclusive city refers both to make it easy for citizens to use urban spaces and to involve citizens in the planning, design and management of cities. Citizens leave a trace when using public space and this information can be known, thanks to the last technological advances in the field of Information and Communication Technology It is the citizens themselves who voluntarily make these data available via social networking websites. It is therefore a new form of public participation. Knowing the real citizens’ use of public space is essential for the planning and design of Smart Cities. However, information in general is not and does not produce knowledge itself. Knowledge does not simply come from having access to large amounts of information. It is necessary to understand the databases and structure the information to ensure proper use of it. This research delves into problem solving on how to collect and how to process this information. Specifically, this paper focuses on obtaining data from social networking websites relating to the sports field. The main goal of this research is to introduce a citizen-centric urban planning approach by analysing the possibilities offered by the citizen-generated data retrieved from social networks for sport. As a result, the graphic representation of aggregated geospatial information in an urban context is proposed in order to improve the decision-making process for its planning and design.This work has been funded by the Conselleria de Educación, Investigación, Cultura y Deporte, of the Community of Valencia, Spain, within the program of support for research under project AICO/2017/134

Flickr Photos Analysis for Beach Tourism Management in Bantul Regency, Indonesia: Popularity and Tourist Attractions

Photos shared by social media users act as an approach in identifying tourist activity. Popular tourist attractions are judged based on the large number of photos or high photo density. In Bantul Regency, Indonesia, beaches have diverse attractions which tourists can enjoy and immortalize through photos. Analyzing the contents of photos on Flickr provides information on the type(s) of beaches or coastal attractions preferred by tourists. This study examined the availability of geotagged Flickr photos to assist in making relevant beach tourism management policies. It employed pattern analysis with the average nearest neighbor, density analysis with kernel density estimation, image content analysis with tourist attraction as the variable, and overlay analysis to formulate recommendations for beach tourism management based on the popularity level of the attractions. The results indicate that each of the local beaches offers different attractions with varying popularity levels and that natural beauty is the main feature attracting tourists to visit all beaches, except Baros. Based on the pattern analysis, the Flickr photos are clustered on several beaches of high popularity, such as Parangtritis, Baros, Depok, and Cemara Sewu. By using geotagged Flickr photo data and refers to the concept of tourism supply and demand, recommendations for developing the attractive features on these beaches have been compiled according to their respective themes and popularity levels to target specific tourist market segments and design integrated tour or travel packages

A Novel Popular Tourist Attraction Discovering Approach Based on Geo-Tagged Social Media Big Data

In the big data era, the social media data that contain users’ geographical locations are growing explosively. These kinds of spatiotemporal data provide a new perspective for us to observe the human movement behavior. By mining such spatiotemporal data, we can incorporate the users’ collective wisdom, build novel services and bring convenience to people. Through spatial clustering of the original user locations, both the ‘natural’ boundaries and the human activity information of the tourist attractions are generated, which facilitate performing popularity analysis of tourist attractions and extracting the travelers’ spatio-temporal patterns or travel laws. On the one hand, the potential extracted knowledge could provide decision supports to the tourism management department in both tourism planning and resource development; on the other hand, the travel preferences are able to be extracted from the clustering-generated attractions, and thus, intelligent tourism recommendation services could be developed for the tourist to promote the realization of ‘smart tourism’. Hence, this paper proposes a new method for discovering popular tourist attractions, which extracts hotspots through integrating spatial clustering and text mining approaches. We carry out tourist attraction discovery experiments based on the Flickr geotagged images within the urban area of Beijing from 2005 to 2016. The results show that compared with the traditional DBSCAN method, this novel approach can distinguish adjacent high-density areas when discovering popular tourist attractions and has better adaptability in the case of an uneven density distribution. In addition, based on the finding results of scenic hotspots, this paper analyzes the popularity distribution laws of Beijing’s tourist attractions under different temporal and weather contexts

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

Identification of central urban attractions based on GPS tracking data and network analysis

[ES]Este estudio presenta una metodología aplicable en la identificación de atracciones turísticas centrales en entornos urbanos mediante el uso combinado de datos GPS y análisis de redes de atracciones visitadas por los turistas. La identificación de las atracciones centrales es fundamental para los gestores de una ciudad, tanto a la hora de planificar las instalaciones y servicios urbanos, o gestionar los recursos municipales, como localizar nuevas atracciones o captar todos los beneficios potenciales de los mismos. El primer paso de la metodología propuesta es la detección de las atracciones visitadas mediante el análisis de datos GPS. A partir de este conjunto de datos GPS se construye una red cuyos nodos son las atracciones visitadas y posteriormente se realiza el análisis de redes correspondiente. El estudio empírico se ha llevado a cabo en la ciudad de Bilbao, destino turístico que ha obtenido fama internacional gracias al Museo Guggenheim. Sorprendentemente, nuestra metodología conduce a resultados inesperados: mientras que los contenidos de las redes sociales (por ejemplo, TripAdvisor) y los expertos (agentes turísticos) señalan al Guggenheim como el principal activo turístico, en realidad resulta ser el Casco Viejo el lugar más visitado de Bilbao según el comportamiento espacial real detectado por nuestro método. Este enfoque metodológico puede servir para tomar decisiones más adaptadas y definir mejores políticas en materia de planificación y gestión urbana.[EN]This study introduces a useful methodology to identify central urban tourism attractions based on the combination of GPS tracking data and the Network Analysis of visited attractions derived from GPS data. Identifying central attractions becomes critical for city managers when it comes to planning urban facilities, managing municipal resources, locating new attractions or capturing all the potential returns. The first step of the proposed methodology is the detection of visited attractions based on GPS tracking data analysis. Then from this GPS data set a network of visited attractions is built in order to carry out a network analysis. The empirical study is performed for the city of Bilbao, a tourism destination made famous by the Guggenheim Museum. Surprisingly, our methodology leads to unexpected results: while social media content (e.g. TripAdvisor) and experts (tourism agents) point to the Guggenheim as the main tourism asset, in fact it turns out to be the Old Town the most visited place in Bilbao according to real spatial behavior detected by our method. This methodological approach can be valuable for performing decisions that are more accurate and better policies concerning urban planning and management.Los autores agradecen la ayuda económica aportada por el Ministerio de Economía y Empresa (MINECOR/FEDER 2015 CREA-NETWORK CSO2015-65265-C4-3-R)

Identificación de atracciones urbanas centrales mediante seguimiento GPS y análisis de redes

Este estudio presenta una metodología aplicable en la identificación de atracciones turísticas centrales en entornos urbanos mediante el uso combinado de datos GPS y análisis de redes de atracciones visitadas por los turistas. La identificación de las atracciones centrales es fundamental para los gestores de una ciudad, tanto a la hora de planificar las instalaciones y servicios urbanos, o gestionar los recursos municipales, como localizar nuevas atracciones o captar todos los beneficios potenciales de los mismos. El primer paso de la metodología propuesta es la detección de las atracciones visitadas mediante el análisis de datos GPS. A partir de este conjunto de datos GPS se construye una red cuyos nodos son las atracciones visitadas y posteriormente se realiza el análisis de redes correspondiente. El estudio empírico se ha llevado a cabo en la ciudad de Bilbao, destino turístico que ha obtenido fama internacional gracias al Museo Guggenheim. Sorprendentemente, nuestra metodología conduce a resultados inesperados: mientras que los contenidos de las redes sociales (por ejemplo, TripAdvisor) y los expertos (agentes turísticos) señalan al Guggenheim como el principal activo turístico, en realidad resulta ser el Casco Viejo el lugar más visitado de Bilbao según el comportamiento espacial real detectado por nuestro método. Este enfoque metodológico puede servir para tomar decisiones más adaptadas y definir mejores políticas en materia de planificación y gestión urbana

Optimal Tourism Development

The early days of tourism development had a naïve vision of tourism’s impacts on society in terms of economic, social, and environmental benefits. Time has passed, and we have learnt lessons regarding the success and failure of tourism development. Mass tourism development has pros and cons and is not necessarily the optimal development model. Alternative development strategies should be contemplated. This Special Issue deals with different topics concerning optimal tourism development. Destination management requires further understanding of different issues, such as carrying capacity, income-based optimal supply size, identification and development of optimal market niches, and adaptation or environmental protection strategies. Tourism planning is concerned with the role of economies of agglomeration, i.e., the advantages of spatial clusters vs scattered development. Additionally, support for and investment in innovation, accessibility, and mobility are relevant nowadays. From the stakeholders’ perspective, it is relevant to discuss ways of cooperating and sources of conflicts among different sectors and actors, governance and incentives for sustainable tourism practices, and equity and economic distribution of benefits. Finally, the development of methodological tools for the assessment of optimal tourism development is necessary for policy making, in particular the development of methods that are capable of integrating economic, environmental, and social criteria