An exploratory visual analytics approach was used to identify temporal distributions, spatial clusters and popular routes of tourists in Amsterdam by making use of geotagged photos from social media platform Flickr. The presented methods combine the analytical strength of humans with the data processing power of computers, using geovisualisations and charts to explore data, find patterns, and draw conclusions from its outcomes. For this research, the metadata of 2,849,261 geotagged photos was harvested from Flickr and stored in a spatial database. From this dataset, 393,828 photos were located in the municipality of Amsterdam. A semi-automatic classification method classified 39,1% of the users as tourist with a very high precision and recall. The temporal distribution of tourists and locals is compared for different temporal granularities. A method is presented to assess photo timestamps by making use of photos that contain a real clock. An existing grid-based clustering method was implemented and improved to explore Amsterdam’s spatial distribution of tourists in Google Earth. The major tourist hotspots are detected using the density-based clustering algorithm DBSCAN. Finally, the most probable routes of tourists between subsequent photo locations were estimated and aggregated into a route density map. A qualitative approach was used to validate the study outcomes by interviewing eight tourism experts of the municipality of Amsterdam. Their knowledge about the city bears a good resemblance with the detected spatial clusters and route density map of tourists. Despite several imperfections of geosocial data, we conclude that the methods provide meaningful insight into the spatial and temporal patterns of tourists in urban spaces and are a valuable addition to traditional tourism surveys.
New forms of value creation are emerging above the radar. Traditional business models wil become obsolete and suffer a slow death. Companies that will create meaningful value and will contribute to the quality of our life and society will grow.
#webcom Montreal - Dragana Djermanovic - Social web: in&out_comeDragana Djermanovic
http://webcom-montreal.com/conferencier/social-web-in-out-come/292/
Le web social est un monde en soi qui obéit à des règles d’ouverture, de transparence et de vitesse. Les participants attendent de la conversation qu’elle leur permette d’améliorer différents aspects de leur vie ou encore de leurs affaires. Dans sa conférence, Dragana Djermanovic abordera certaines facettes des tendances web spécifiques aux habitants des Balkans, les examinera en parallèle avec les tendances mondiales et fournira les exemples les plus marquants de l’utilisation des principes du web social dans le dialogue entre les organisations et les utilisateurs de l’Internet.
Straight Line Thinking Stops Here. Designing business success in a non-linear...SMLXL Ltd
The keynote that I gave at sxsw 2010, Straight Line Thinking Stops Here. Based on the forthcoming book, No Straight Lines, making sense of our non-linear world.
This is from the book the Experience Economy. I was playing with my new macbook pro and thought i could do a nice visual to post. Considering we all talk about the brand experience at the moment. Its an interesting way to model how your create and continue to build your brand experiences
New forms of value creation are emerging above the radar. Traditional business models wil become obsolete and suffer a slow death. Companies that will create meaningful value and will contribute to the quality of our life and society will grow.
#webcom Montreal - Dragana Djermanovic - Social web: in&out_comeDragana Djermanovic
http://webcom-montreal.com/conferencier/social-web-in-out-come/292/
Le web social est un monde en soi qui obéit à des règles d’ouverture, de transparence et de vitesse. Les participants attendent de la conversation qu’elle leur permette d’améliorer différents aspects de leur vie ou encore de leurs affaires. Dans sa conférence, Dragana Djermanovic abordera certaines facettes des tendances web spécifiques aux habitants des Balkans, les examinera en parallèle avec les tendances mondiales et fournira les exemples les plus marquants de l’utilisation des principes du web social dans le dialogue entre les organisations et les utilisateurs de l’Internet.
Straight Line Thinking Stops Here. Designing business success in a non-linear...SMLXL Ltd
The keynote that I gave at sxsw 2010, Straight Line Thinking Stops Here. Based on the forthcoming book, No Straight Lines, making sense of our non-linear world.
This is from the book the Experience Economy. I was playing with my new macbook pro and thought i could do a nice visual to post. Considering we all talk about the brand experience at the moment. Its an interesting way to model how your create and continue to build your brand experiences
Reshape your business to become disruption proof. Today every business should challenge it's purpose, it's value proposition and business model to become future-proof. Products and services are becoming increasingly interchangeable. Almost any industry is feeling the urgency. Old handles do not work anymore and straightline thinking stops here. One has to make sure that one delivers meaningful experiences that capture value for clients and all stakeholders. Explore the possibilities to become certified experience professional. Registration for the new program 2016 is open.
Digital Trends in 2017: Making Business Impact in a Changing WorldEdelman
Edelman Digital's 2017 report focuses on what we see as the
growing considerations that will impact brands.
Based on changes we observed in 2016, we’ll explore areas such as paid, search, influencers, conversational technologies, B2B
and others.
ΑΑΔΕ: Κοινοποίηση της αρ. πρωτ. ΔΕΦΚΦ Δ 1189980ΕΞ 2016 (29-12-16) απόφασης ΓΓΔΕ "Διαδικασία καταβολής ΦΠΑ για αυτοκίνητα οχήματα καινούρια και μεταχειρισμένα που παραλαμβάνονται από δικαιούχα απαλλαγής από το τέλος ταξινόμησης πρόσωπα κατόπιν μεταβίβασής τους από τις εταιρείες εμπορίας αυτοκινήτων"
By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.
Reshape your business to become disruption proof. Today every business should challenge it's purpose, it's value proposition and business model to become future-proof. Products and services are becoming increasingly interchangeable. Almost any industry is feeling the urgency. Old handles do not work anymore and straightline thinking stops here. One has to make sure that one delivers meaningful experiences that capture value for clients and all stakeholders. Explore the possibilities to become certified experience professional. Registration for the new program 2016 is open.
Digital Trends in 2017: Making Business Impact in a Changing WorldEdelman
Edelman Digital's 2017 report focuses on what we see as the
growing considerations that will impact brands.
Based on changes we observed in 2016, we’ll explore areas such as paid, search, influencers, conversational technologies, B2B
and others.
ΑΑΔΕ: Κοινοποίηση της αρ. πρωτ. ΔΕΦΚΦ Δ 1189980ΕΞ 2016 (29-12-16) απόφασης ΓΓΔΕ "Διαδικασία καταβολής ΦΠΑ για αυτοκίνητα οχήματα καινούρια και μεταχειρισμένα που παραλαμβάνονται από δικαιούχα απαλλαγής από το τέλος ταξινόμησης πρόσωπα κατόπιν μεταβίβασής τους από τις εταιρείες εμπορίας αυτοκινήτων"
By integrating new techniques in data mining and operational research, we develop a novel travel planning system to design multi-day and multi-stay travel plans based on geo-tagged photos. Specifically, a modified Iterated Local Search heuristic algorithm is developed to find an approximate optimal solution for the multi-day and multi-stay travel planning problem using points of interests (POIs) and recurrence weights between POIs in a travel graph model, which are discovered from photos. To demonstrate the feasibility of this approach, we retrieved geo-tagged photos in Australia from the photo sharing website Panoromia.com to design experimental multi-day and multi-stay travel plans for tourists. The travel patterns that are mined using flow-mapping technique at different geographical scales are used to evaluate the experimental results.
Slides: Safeguarding Abila through Multiple Data PerspectivesParang Saraf
Abstract: This paper introduces a system for visual analysis of news articles, emails, GPS tracking data, financial transactions and streaming micro-blog data. The system was developed in response to the 2014 VAST Grand Challenge and comprises of several interfaces for mining textual, network, spatio-temporal, financial, and streaming data.
For more information, please visit: http://people.cs.vt.edu/parang/ or contact parang at firstname at cs vt edu
The impact of temporal resolution on the precision of accessibility measurementMarcin Stępniak
Slides presented at the event Accessibility in urban modelling: from measurement to policy instruction co-organized by NECTAR Cluster 6 and Urban Europe Research Alliance (UERA).
Lyon, June 18th-20th, 2018
MARKET ANALYSIS IN GREECE REGARDING THE FILED OF TOURISMmakisb1
Tourism is of high importance for the economy of a country
Many organizations record and analyze tourism data
The current manuscript statistically analyzes the tourism in Greece for the years from 2007 to 2016:
Using the simple linear trend model, we make a forecast for tourist nights spend in Greece till 2021, using time series analysis. We also use the regression analysis as another method (night spend by arrivals).
In machine learning, we make a forecast for nights spend in Greece using regression analysis (night spend by arrivals)
Collecting consumer insights using app-context fencing - PollfishMerlien Institute
at Market Research in the Mobile World Europe
23 - 26 September 2014, Belin, Germany
This event is proudly organised by Merlien Institute
Check out our upcoming events by visiting http://www.mrmw.net
Location Embeddings for Next Trip RecommendationRaphael Troncy
Joint work wih Amadeus presenting a recommender system for your next destination using knowledge graphs and deep learning network, presented at the LocWeb 2019 Workshop colocated with TheWebConf 2019 (San Francisco, USA)
In this talk I will consider the analysis of social media data in an urban context, in particular we look at textual data, visual data and all their metadata to understand social and business phenomena. Analyzing such complex and diverse data poses major challenges for the analyst as the insight of interest is a result of an intricate interplay between the different modalities, their metadata and the evolving knowledge the analyst has about the problem. Our multimedia analytics solutions brings together automatic multimedia analysis and information visualization to give the analyst the optimal opportunities to get insight in complex datasets and use them in applications such as recommending venues to tourists, measuring the effect of city marketing campaigns, or seeing how social multimedia redefines urban borders.
Similar to Revealing spatial and temporal patterns from Flickr photography: a case study with tourists in Amsterdam (20)
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
3. MORE AND MORE CONCERNS ABOUT TOURISM
A SELECTION OF RECENT NEWS ARTICLES
They are puking and peeing on the Zeedijk
NOS, December 5 2014
Is Amsterdam becoming a second Venice?
De Morgen, March 27 2015
The center of Amsterdam should not become too popular
Volkskrant, October 25 2014
Amsterdam taken over by tourists
RTL, April 3 2015
Amsterdam will welcome twice as many tourists in 2030
Het Parool, December 9 2014
4. INITIAL RESEARCH TOPIC
WAGENINGEN UNIVERSITY AND AMS
Explore the possibilities to use (geo)tweets for detecting
spatial and temporal patterns of tourists in Amsterdam
But why Twitter? How about Flickr?
Twitter Flickr
Number of users + + + / -
Amount of data + + +
Connection of data to real location + / - + +
Use by tourists + / - + +
Interval between subsequent posts + / - + +
5. RESEARCH PROJECT
The objective of this exploratory research project is to develop,
implement and test methods that reveal spatial and temporal patterns
of tourists from a large dataset of geotagged Flickr photos
OBJECTIVE
RESEARCH QUESTIONS
RQ-01: What methods are available to detect spatial and temporal
patterns from geosocial data?
RQ-02: What methods need to be implemented to identify
temporal distributions, spatial clusters and popular routes of
tourists from the metadata of Flickr photos?
RQ-03: How well do the identified temporal distributions, spatial
clusters and popular routes resemble the spatial and temporal
behaviour of tourists?
7. FLICKR DATA COLLECTION
OVERVIEW OF STEPS & TECHNIQUES
Flickr Database
(API)
Request
Local database
(PostgreSQL)
Java application
XML-file
Metadata
Restriction: 1 request per second
8. FLICKR DATA COLLECTION
STEP 1: HARVESTING PHOTO ID’S WITHIN BOUNDING BOXES (1550)
Search parameters:
• Xmin, Xmax, Ymin, Ymax
• Min date: January 1, 2005
• Max date: December 31, 2014
Search result:
• Photo ID
• User ID
• Photo title
9. FLICKR DATA COLLECTION
STEP 2: REQUESTING ADDITIONAL METADATA
Search parameters:
• Photo ID
Search result:
• Latitude, longitude
• Date and time
• User name
• User home location
• Tags
• Photo URL
• Location accuracy
2.849.261 photos
+/- 5 weeks of harvesting
10. FLICKR DATA COLLECTION
STEP 2: REQUESTING ADDITIONAL METADATA
Search parameters:
• Photo ID
484.346 photos
Search result:
• Latitude, longitude
• Date and time
• User name
• User home location
• Tags
• Photo URL
• Location accuracy
14. TOURIST CLASSIFICATION
1. Classification of user location by SQL
UPDATE users
SET countryname = 'Japan', istourist = 'True', classification = 'SQL'
WHERE geoname = '' AND userid IN
(SELECT userid FROM users WHERE (userlocation ~* 'y(japan|nippon|日本)y'))
(8628 users - 54%)
SQL AND ONLINE GEOCODING
Geonames API
(External database)
PostgreSQL
(Local database)
Java Application
2. Classification of user location by online geocoding
Tokyo Tokyo
Japan Japan
(450 users - 3%)
User location = Tokyo Tokyo = Japan
16. NUMBER OF UNIQUE PHOTOS
0
40.000
80.000
120.000
160.000
132.213
107.016
154.599
39,3% 27,2% 33,6%
Local Photos Tourist Photos Unclassified Photos
TOURIST CLASSIFICATION
Overall accuracy = 99%
17. CLASSIFICATION RESULTS AMSTERDAM
RELATIVE AMOUNT OF TOURISTS PER NATIONALITY (2013)
United States
United Kingdom
Germany
Italy
Spain
France
0% 5% 10% 15% 20%
Flickr nationalities 2013
CBS hotel nationalities 2013
19. TEMPORAL DISTRIBUTIONS
RELATIVE NUMBER OF TOURISTS AND PHOTOS PER HOUR (2005-2014)
0%
2%
4%
6%
8%
10%
1:00
2:00
3:00
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
0:00
Tourists
Tourist photos
Many daytime
photos
20. TEMPORAL DISTRIBUTIONS
RELATIVE NUMBER OF TOURISTS AND LOCALS PER HOUR (2005-2014)
0%
2%
4%
6%
8%
10%
1:00
2:00
3:00
4:00
5:00
6:00
7:00
8:00
9:00
10:00
11:00
12:00
13:00
14:00
15:00
16:00
17:00
18:00
19:00
20:00
21:00
22:00
23:00
0:00
Tourists
Locals
Maximums shifted
Relatively more
tourists photos
in the night
More local
photos in
the evening
21. Exact match
2 hours off
TIMESTAMP VALIDATION
TIME DIFFERENCE BETWEEN PHOTO TIMESTAMP AND REAL TIME
22. TIMESTAMP VALIDATION
TIME DIFFERENCE BETWEEN PHOTO TIMESTAMP AND REAL TIME
Selecting
• all photos tagged with ‘clock’
• all photos near Central Station
!
1032 photos of locals
1134 photos of tourists
Result
• 70 suitable photos of tourists
• 50 suitable photos of locals
24. PHOTOGRAPHERS PER DAY OF THE WEEK (2005-2014)
0%
5%
10%
15%
20% Monday
Tuesday
Wednesday
Thursday
Friday
Saturday
Sunday
Tourists
Locals
TEMPORAL DISTRIBUTIONS
25. PHOTOGRAPHERS PER MONTH (2005-2014)
0%
2%
4%
6%
8%
10%
12%
January
February
March
April
May
June
July
August
September
October
November
December
Tourists
Locals
TEMPORAL DISTRIBUTIONS
26. TOURISTS AND FOREIGN HOTEL GUESTS PER MONTH (2012+2013)
0%
2%
4%
6%
8%
10%
12%
January
February
March
April
May
June
July
August
September
October
November
December
Tourists (Flickr 2012 + 2013)
Hotel guests (CBS 2012 + 2013)
TEMPORAL DISTRIBUTIONS
32. SPATIAL DISTRIBUTION
DENSITY-BASED CLUSTERING
DBSCAN: Density-Based Spatial Clustering for Applications with Noise
• Detects clusters with different shapes and sizes
• Not sensitive to noise very suitable for geosocial data
!
• Eps: radius search area
• MinPts: minimum number of points in neighborhood
Eps
Noise
MinPts=4
45. STEP 2: REDUCE TRAVEL COST PER ROAD SEGMENT BASED ON PHOTO DENSITY
TOURISTIC ROUTES
2,6
1,9
1,4
4,2
3,1
1,8
6,9
6,2
4,1
7,3
9,3
9,6
46. 1. Create pairs of time-ordered photo locations per user
Point A Point B
Point B Point C
… …
!
2. Calculate distance, time interval and speed per photo pair
3. Select all photo pairs within thresholds:
• Distance > 50 m and < 750 m
• Time interval > 0 sec and < 600 sec
• Speed > 1 km/h and < 5 km/h
4. Calculate closest network node for start and end of every pair
TOURISTIC ROUTES
STEP 3: CREATE PHOTO PAIRS FOR ROUTING
47. TOURISTIC ROUTES
STEP 4: CALCULATE ROUTES AND AGGREGATE INTO ROUTE DENSITY MAP
1. Calculate route for 6,477 photo pairs with pgRouting
2. Aggregate and count overlaying route segments
3. Visualize touristic route densities
48. TOURISTIC CLUSTERS AND ROUTES
VALIDATION OF RESULTS
Solution: Expert judgement by a questionnaire
Participants: 8 tourism experts from different departments of the
municipality of Amsterdam
Problem: No comparable quantitative data available
49. TOURISTIC ROUTES
VALIDATION OF RESULTS BY 8 TOURISM EXPERTS
Match: 75% Match: 38% Match: 75%
Match: 100% Match: 100% Match: 63%
Match: 100% Match: 67% Match: 67%
Match: 100% Match: 100% Match: 100%
WITH HIGH CONFIDENCE (5/5)3
50. VALIDATION OF RESULTS
TOURISTIC CLUSTERS AND ROUTES
Expert # Profession
Validity
results [1-5]
Usefulness
results [1-5]
1 Policy Advisor Traffic & Public Space 4 5
2 Data Analyst, Information en Statistics 4 4
3 Senior Advisor Traffic Management 4 4
4 Researcher, Information en Statistics 3 4
5 Senior Advisor Traffic Research 5 4
6 Urban Planner 5 5
7 Urban Planner 4 5
8 Urban Designer 4 5
4.1 4.5
How well do the study outcomes resemble the real world?
Are the study outcomes useful for you or for your organization?
*
**
* **
51. SUGGESTIONS FOR FUTURE WORK
AND POTENTIAL THESIS TOPICS
• Calibrate thresholds with quantitative data
• Extensive validation of results in cooperation with tourism experts
• Cooperate with municipality to define objectives, some suggestions:
Additional data sources: Instagram, Twitter, Sina Weibo
Divide spatial distributions in different temporal intervals
Compare spatial distribution of locals and tourists
Divide the spatial distributions in different nationalities
Use the presented patterns as input for an agent-based model
Discover typical tourism problems with other geosocial data types
52. THANK YOU FOR YOUR ATTENTION!
ANY QUESTIONS OR REMARKS?