| 1. |
- Fuchs, Matthias, 1970-, et al.
(författare)
-
Using Google Maps Data for Tourism Real-Time Monitoring and Analytics : The case of Cultural Tourism, Sweden
- 2021
-
Konferensbidrag (refereegranskat)abstract
- Although globally Google Maps ranges among the most popular web-portals for travel information search, tourism studies using Google Maps data are scant. We are reporting about ongoing research conducted in Sweden aiming both, at reliably monitoring the fragmented cultural tourism offer as well as analyzing travelers’ complex cultural tourism experience by using Google Maps data. More precisely, the supply-side monitoring estimates and visualizes Google places labelled by suppliers as ‘cultural’, thereby revealing regional patterns and the geographical distribution of the cultural tourism offer all over Sweden. By contrast, in order to reveal travelers’ experience outcomes, demand-side analytics focus on user generated content [UGC] analysis by applying sentiment analysis and topic detection, respectively. Before discussing the findings, we briefly outline the used methods for data extraction: First, a grid-field for Sweden created with the geographical information system ArcGIS served as input for the Google Maps places API to retrieve 115,316 Google places. Subsequently, a sub-total of 13,915 place types relevant for cultural tourism was identified through manual annotation. Finally, place types were mapped to cultural tourism place categories as proposed by the literature, such as heritage, arts, religious, and natural heritage tourism, respectively. With regard to UGC analytics, the web-crawler Scrapy was employed to extract 353,960 tuples with review-text related to cultural tourism places. While for sentiment analysis a lexicon-based approach using Liu’s (2020) famous wordlists for positive and negative tonality was employed, Latent Dirichlet Allocation (LDC) was applied to deduce topic-clusters that best represent cultural tourism categories. As a final step, retrieved topic-clusters were mapped to place types enriched by UGC-based sentiment. By using the visualization software Tableau, findings show the share and geographical distribution of cultural tourism place categories for Sweden (national view) as well as for cultural tourism place types for each Swedish region (regional view). Moreover, most popular as well as top-rated topic-clusters along with most frequent opinion words can be displayed for each cultural tourism place type. Most notably, sentiment distribution (i.e. positive, negative, and neutral) can be shown for place categories for each region and over time. We conclude that most relevant analysis perspectives for real-time tourism monitoring and analytics are adequately supported by the inexpensive Google Maps data. As limitation, we point at potential representativeness issues, as 56% of data sets do not comprise any review. For future research, we envisage to also include adjectives from UGC data for better grasping travelers’ complex cultural tourism experience. Finally, we propose the analysis of traveler’s spatial behavior and movement patterns by employing association rule analysis and sequential pattern mining, respectively.
|
|
| 2. |
- Höpken, Wolfram, et al.
(författare)
-
Google Trends data for analysing tourists’ online search behaviour and improving demand forecasting : The case of Åre, Sweden
- 2019
-
Ingår i: Information Technology & Tourism. - : Springer. - 1098-3058 .- 1943-4294. ; 21:1, s. 45-62
-
Tidskriftsartikel (refereegranskat)abstract
- Accurate forecasting of tourism demand is of utmost relevance for the success oftourism businesses. This paper presents a novel approach that extends autoregressiveforecasting models by considering travellers’ web search behaviour as additionalinput for predicting tourist arrivals. More precisely, the study presents a methodwith the capacity to identify relevant search terms and time lags (i.e. time differencebetween web search activities and tourist arrivals), and to aggregate these timeseries into an overall web search index with maximal forecasting power on tourismarrivals. The proposed approach enables a thorough analysis of temporal relationshipsbetween search terms and tourist arrivals, thus, identifying patterns that reflectonline planning behaviour of travellers before visiting a destination. The study isconducted at the leading Swedish mountain destination, Åre, using arrival data andGoogle web search data for the period 2005–2012. Findings demonstrate the abilityof the proposed approach to outperform traditional autoregressive approaches, byincreasing the predictive power in forecasting tourism demand.
|
|
| 3. |
- Höpken, Wolfram, et al.
(författare)
-
Improving Tourist Arrival Prediction : A Big Data and Artificial Neural Network Approach
- 2021
-
Ingår i: Journal of Travel Research. - : SAGE Publications. - 0047-2875 .- 1552-6763. ; 60:5, s. 998-1017
-
Tidskriftsartikel (refereegranskat)abstract
- Because of high fluctuations of tourism demand, accurate predictions of tourist arrivals are of high importance for tourism organizations. The study at hand presents an approach to enhance autoregressive prediction models by including travelers’ web search traffic as external input attribute for tourist arrival prediction. The study proposes a novel method to identify relevant search terms and to aggregate them into a compound web-search index, used as additional input of an autoregressive prediction approach. As methods to predict tourism arrivals, the study compares autoregressive integrated moving average (ARIMA) models with the machine learning–based technique artificial neural network (ANN). Study results show that (1) Google Trends data, mirroring traveler’s online search behavior (i.e., big data information source), significantly increase the performance of tourist arrival prediction compared to autoregressive approaches using past arrivals alone, and (2) the machine learning technique ANN has the capacity to outperform ARIMA models.
|
|
| 4. |
- Richter, Katharina N., et al.
(författare)
-
Glyoxal as an alternative fixative to formaldehyde in immunostaining and super-resolution microscopy
- 2018
-
Ingår i: EMBO Journal. - : WILEY. - 0261-4189 .- 1460-2075. ; 37:1, s. 139-159
-
Tidskriftsartikel (refereegranskat)abstract
- Paraformaldehyde (PFA) is the most commonly used fixative for immunostaining of cells, but has been associated with various problems, ranging from loss of antigenicity to changes in morphology during fixation. We show here that the small dialdehyde glyoxal can successfully replace PFA. Despite being less toxic than PFA, and, as most aldehydes, likely usable as a fixative, glyoxal has not yet been systematically tried in modern fluorescence microscopy. Here, we tested and optimized glyoxal fixation and surprisingly found it to be more efficient than PFA-based protocols. Glyoxal acted faster than PFA, cross-linked proteins more effectively, and improved the preservation of cellular morphology. We validated glyoxal fixation in multiple laboratories against different PFA-based protocols and confirmed that it enabled better immunostainings for a majority of the targets. Our data therefore support that glyoxal can be a valuable alternative to PFA for immunostaining.
|
|
