Using Social Media Data to Map Mortar Shells Falling in Damascus, Syria

https://doi.org/10.35595/2414-9179-2021-2-27-233-240

Посмотреть или загрузить статью (Eng)

Об авторе

Mohamad Hasan

Saint Petersburg University,
Sredniy prospekt V.O., 41, 199034, Saint-Petersburg, Russia;
E-mail: mo-hasan89@hotmail.com

Аннотация

The paper analyzes the use of social media data in geographical information systems to map the areas most affected by mortar shells in the capital of Syria, Damascus, by using geocoded and parsed social media data in geographical information systems. This paper describes a created algorithm to collecting and store data from social media sites. For the data store both a NoSQL database to save JSON format document and an RDBMS is used to save other spatial data types. A python script was written to collect the data in social media based on certain keywords related to the search. A geocoding algorithm to locate social media posts that normalize, standardize and tokenize the text was developed. The result of the developed diagram provided a year by year from 2013 to 2018 maps for mortar shell falling locations in Damascus. These layers give an overview for the changing of the numbers of mortar shells falls or in hot spot analysis for the city. Finally, social media data can prove to be useful when creating maps for dynamic social phenomena, for example, mortar shells’ location falling in Damascus, Syria. Moreover, social media data provide easy, massive, and timestamped data which makes these phenomena easier to study.

Ключ. слова

data mining, geocoding, NoSQL databases, social media.

Список литературы

  1. Alexander D.E. Social media in disaster risk reduction and crisis management. Science and engineering. 2014. V. 20, Issue. 3. P. 717–733.
  2. Alharith A., Samak Y. Fighting terrorism more effectively with the aid of GIS: Kingdom of Saudi Arabia case study. American journal of geographic information system, 2018. V. 7, Issue. 1. P. 15–31.
  3. Barbier G., Goolsby R., Gao H. Harnessing the Crowdsourcing power of social media for disaster relief. IEEE intelligent systems, 2011. V. 26, Issue. 3. P. 10–14.
  4. Brake D.R. Are we all online content creators now? web 2.0 and digital divides. Journal of computer-mediated communication, 2014. V. 19, Issue. 3. P. 591–609.
  5. Churches T., Christen P., Lim K., Zhu J.X. Preparation of name and address data for record linkage using hidden Markov models. BMC Medical Informatics and Decision Making, 2002. V. 2. P. 1–16.
  6. Golubovic N., Krintz C., Wolski R., Lafia S., Hervey T., Kuhn W. Extracting spatial information from social media in support of agricultural management decisions. proceedings of the 10th Workshop on Geographic Information Retrieval, 2017, Burlingame, California: GIR’16.
  7. Goodchild M., Glennon A. Crowdsourcing geographic information for disaster response: A research frontier. International journal of digital Earth, 2010. V. 3, Issue. 3. P. 231–241.
  8. Hay S.I., George D.B., Moyes C.L., Brownstein J.S. Big data opportunities for global infectious disease surveillance. PLoS medicine, 2013. V. 10, Issue. 4, DOI: 10.1371/journal.pmed.1001413.
  9. Hecht B., Hong L., Suh B. Tweets from Justin Bieber’s heart: The dynamics of the location field in user profiles. Proceedings of the 2011 annual conference on human factors in computing systems, 2011, P. 237–246.
  10. Hill L. Core elements of digital gazetteers: placenames, categories, and footprints. Research and advanced technology for digital libraries, 2000. V. 1923. P. 280–290.
  11. Huang Q., Yu Xiao Y. Geographic situational awareness: mining tweets for disaster preparedness, emergency response, impact, and recovery. ISPRS international journal of geo-information, 2015. V. 4, Issue: 3. P. 1549–1568.
  12. Kaplan A., Haenlein M. Users of the world, unite! the challenges and opportunities of social media. Business Horizons, 2010. V. 53. P. 59–68.
  13. Sakaki T., Okazaki M. Earthquake shakes twitter users: Real-time event detection by social sensors. Proceedings of the 19th international conference on world wide web, 2010. P. 851–860.
  14. Schradie J. The digital production gap: The digital divide and web 2.0 collide. poetics, 2011. V. 39, Issue. 2. P. 145–168.
  15. Simon T., Goldberg A., Adini B. Socializing in emergencies, A review of the use of social media in emergency situations. International journal of information management, 2015. V. 35, Issue. 1. P. 609–619.
  16. Soulis K., Varlamis I., Giannakoulopoulos A., Charatsev F. A tool for the visualization of public opinion. International journal of electronic governance, 2013. V. 6, Issue. 3. P. 218–231.
  17. Smith A. Why Americans use social media. Technical report, Pew Research Centre. URL: http://www.pewinternet.org/Reports/2011/WhyAmericans-Use-Social-Media.aspx, visit date: 12/4/2019.

For citation: Hasan M. Using Social Media Data to Map Mortar Shells Falling in Damascus, Syria InterCarto. InterGIS. GI support of sustainable development of territories: Proceedings of the International conference. Moscow: MSU, Faculty of Geography, 2021. V. 27. Part 2. P. 233–240. DOI: 10.35595/2414-9179-2021-2-27-233-240