Location identification for crime &amp;amp; disaster events by geoparsing Twitter

Dhavase, Nikhil; Bagade, Anant M.

doi:10.1109/i2ct.2014.7092336

Cited by 20 publications

(13 citation statements)

References 5 publications

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“…The work has implications beyond the measurement of road crashes to include any events of interest reported on social media. For example, it can be applied to other topics such as disaster relief, a prominent use of geoparsing of tweets, especially in the context of disaster response [4,5]. Improved algorithms can lead to a faster and better geolocation of events in areas affected by disasters, allowing, e.g., for improved response and disaster management.…”

Section: Results and Policy Implicationsmentioning

confidence: 99%

Can crowdsourcing create the missing crash data?

Milusheva

Marty

Bedoya

et al. 2020

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

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UPDATED-June 1, 2020. Road traffic crashes (RTCs) are the primary cause of death among children and young adults. Yet data on RTCs is incomplete, hindering effective road safety policymaking in many developing countries where mortality is purportedly highest. We web-scrape 850,000 tweets to create crash data and develop a machine learning algorithm to geolocate RTCs. Our algorithm is nearly twice as precise as a standard geoparsing algorithm in identifying the set of locations that include the crash location. Above and beyond, it identifies the unique location of a crash from the set of possible locations in a majority of cases. We dispatch a set of motorcycle drivers to the site of the presumed crash in real time to verify the validity of the crowdsourced data and document the performance of the algorithm. The study can be used as a proof of concept for countries interested to improve RTC data at low cost through a machine learning approach and substantially increase the data available to analyze RTCs and prioritize road safety policies. CCS CONCEPTS• Computing methodologies → Machine learning algorithms.

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Section: Results and Policy Implicationsmentioning

confidence: 99%

Can crowdsourcing create the missing crash data?

Milusheva

Marty

Bedoya

et al. 2020

Proceedings of the 3rd ACM SIGCAS Conference on Computing and Sustainable Societies

View full text Add to dashboard Cite

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“…Another approach to monitoring that involves the classification of tweets is the use of spatio-temporal features located within tweets. [15] employ a three-stage approach by parsing location-related information contained in tweets. Initially, speech tagging and chunking is performed to understand the structure of each tweet.…”

Section: Classificationmentioning

confidence: 99%

The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing

Loynes¹,

Ouenniche

Smedt

2020

Ann Oper Res

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This paper provides the humanitarian community with an automated tool that can detect a disaster using tweets posted on Twitter, alongside a portal to identify local and regional Non-Governmental Organisations (NGOs) that are best-positioned to provide support to people adversely affected by a disaster. The proposed disaster detection tool uses a linear Support Vector Classifier (SVC) to detect man-made and natural disasters, and a Density-based spatial clustering of applications with noise (DBSCAN) algorithm to accurately estimate a disaster's geographic location. This paper provides two original contributions. The first is combining the automated disaster detection tool with the prototype portal for NGO identification. This unique combination could help reduce the time taken to raise awareness of the disaster detected, improve the coordination of aid, increase the amount of aid delivered as a percentage of initial donations and improve aid effectiveness. The second contribution is a general framework that categorises the different approaches that can be adopted for disaster detection. Furthermore, this paper uses responses obtained from an onthe-ground survey with NGOs in the disaster-hit region of Uttar Pradesh, India, to provide actionable insights into how the portal can be developed further.

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“…The approach can be extended to other events reported on social media, whether related to disaster relief, crime, personal safety, urban mobility, or road maintenance. The work on disaster relief and response makes prominent use of geoparsing of tweets [36][37][38][39][40][41][42][43]. Geoparsing of tweets that lack geolocation information could enable more comprehensive crime analytics [44][45][46].…”

Section: Introductionmentioning

confidence: 99%

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning

et al. 2021

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With all the recent attention focused on big data, it is easy to overlook that basic vital statistics remain difficult to obtain in most of the world. What makes this frustrating is that private companies hold potentially useful data, but it is not accessible by the people who can use it to track poverty, reduce disease, or build urban infrastructure. This project set out to test whether we can transform an openly available dataset (Twitter) into a resource for urban planning and development. We test our hypothesis by creating road traffic crash location data, which is scarce in most resource-poor environments but essential for addressing the number one cause of mortality for children over five and young adults. The research project scraped 874,588 traffic related tweets in Nairobi, Kenya, applied a machine learning model to capture the occurrence of a crash, and developed an improved geoparsing algorithm to identify its location. We geolocate 32,991 crash reports in Twitter for 2012–2020 and cluster them into 22,872 unique crashes during this period. For a subset of crashes reported on Twitter, a motorcycle delivery service was dispatched in real-time to verify the crash and its location; the results show 92% accuracy. To our knowledge this is the first geolocated dataset of crashes for the city and allowed us to produce the first crash map for Nairobi. Using a spatial clustering algorithm, we are able to locate portions of the road network (<1%) where 50% of the crashes identified occurred. Even with limitations in the representativeness of the data, the results can provide urban planners with useful information that can be used to target road safety improvements where resources are limited. The work shows how twitter data might be used to create other types of essential data for urban planning in resource poor environments.

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Location identification for crime & disaster events by geoparsing Twitter

Cited by 20 publications

References 5 publications

Can crowdsourcing create the missing crash data?

Can crowdsourcing create the missing crash data?

The detection and location estimation of disasters using Twitter and the identification of Non-Governmental Organisations using crowdsourcing

Applying machine learning and geolocation techniques to social media data (Twitter) to develop a resource for urban planning

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