VAST Challenge – Mini Challenge 3

Demo: https://idatavisualizationlab.github.io/VAST2019mc3/

Report: https://idatavisualizationlab.github.io/VAST2019mc3/TTU-Nguyen-MC3/index.htm

Seismic and survey data are useful for capturing the objective damage that the earthquake has caused St. Himark. However", "this data has limitations. First", "official surveys are time consuming and do not stay current in a rapidly changing situation. Second", "they don’t establish how citizens are reacting to the current crisis. Third", "they are often insufficiently granular", "providing little insight into differences between neighborhoods. In other words", "the seismic and survey data do not provide an up-to-date view of the structural and humanitarian impact caused by the earthquake on a neighborhood-by-neighborhood basis. The City has concluded that this knowledge is necessary to determine where to allocate emergency resources.

City Officials have identified a subset of Y*INT", "a community-based social media platform", "as a potential source for revealing the current state of St. Himark’s neighborhoods and people. Knowing that you are skilled in visual analytics", "the City has asked you to analyze Y*INT messages in order to determine the appropriate actions it must take in order to assist the community in this disaster.

Tasks and Questions: The City has been using Y*INT to communicate with its citizens", "even post-earthquake. However", "City officials needs additional information to determine the best way to allocate emergency resources across all neighborhoods of St. Himark. Your task", "using your visual analytics on the community Y*INT data", "is to determine the types of problems that are occurring across the St. Himark. Then", "advise the City on how to prioritize the distribution of resources. Keep in mind that not all sources on Y*INT are reliable", "and that priorities may change over time as the state of neighborhoods also changes.

Task 1 [T1] - Using visual analytics", "characterize conditions across the city and recommend how resources should be allocated at 5 hours and 30 hours after the earthquake. Include evidence from the data to support these recommendations. Consider how to allocate resources such as road crews", "sewer repair crews", "power", "and rescue teams. Limit your response to 1000 words and 12 images.
Task 2 [T2] - Identify at least 3 times when conditions change in a way that warrants a re-allocation of city resources. What were the conditions before and after the inflection point? What locations were affected? Which resources are involved? Limit your response to 1000 words and 10 images.
Task 3 [T3] - Take the pulse of the community. How has the earthquake affect life in St. Himark? What is the community experiencing outside the realm of the first two questions? Show decision makers summary information and relevant/characteristic examples. Limit your response to 800 words and 8 images
Task 4 [T4] - The data for this challenge can be analyzed either as a static collection or as a dynamic stream of data", "as it would occur in a real emergency. Describe how you analyzed the data - as a static collection or a stream. How do you think this choice affected your analysis? Limit your response to 200 words and 3 images.

Main points:

(1) determine the types of problems
(2) prioritize the distribution of resources -> resource allocation
(3) not all sources on Y*INT are reliable -> uncertainty
(4) priorities may change over time
(5) state of neighborhoods also changes

Plan:

0. Data characteristic

Each record contains:

Timestamp (date-time)
Location
Account
Content

The content also contains #hashtag", "which will be useful in many cases. These hashtags will be treated the same as ones of twitter.

Domain specific: Take a look through all records.

1. Data cleaning

Input data is from a social media platform (tweeter-like). The process of data cleaning includes

Filter out spam/unreliable posts. The accounts that provide these posts also need to be omitted.
Lemmatize and stemming root words.
Use hashtag to match and omit message.

2. Data extraction - problem perspective

These main points lead to:

(1) determine the types of problems

1a. The problems can be extracted from:

Repetitive keywords
Words from 2 kinds of accounts: The one with high level (degree) or the one who connects the community (these kind of accounts can be detected by force directed network)

1b. Types of problems: From the problems extracted from (1a)", "classify them based on domain algorithm or domain documentation of rescue/disaster.

This can use TimeArcs to detect cluster/community/issues over time.

(2) prioritize the distribution of resources -> resource allocation

2a. Need to research resource allocation algorithm.

3a. Need to research reliability info from social media topic.

4a. Attached to 2a

5a. Can use WordStream to detect changes in context of neighborhood.

3. Data extraction - data perspective

To get a broad view of the data", "get into the attributes:

Timestamp:

When do people tweet the most?

Location:

Where do people tweet the most?
Number of accounts per each location?
Main accounts at each location? <- find out who have big roles: high level", "connect communities",...

Account:

What accounts tweet the most? Where? When? What are their networks?

Message:

Do the messages of this location have some special pattern? i.e. Apart from usual norm for tweets and social media posts", "is there any typical/special points about messages from this city? -> Domain specific. Slang", "...

Categories https://www.humanitarianresponse.info/en/coordination/clusters/global-cluster-coordinators-group-0

https://www.humanitarianresponse.info/en/about-clusters/what-is-the-cluster-approach

https://www.humanitarianresponse.info/sites/www.humanitarianresponse.info/files/documents/files/IASC%20Guidance%20Note%20on%20using%20the%20Cluster%20Approach%20to%20Strengthen%20Humanitarian%20Response%20%28November%202006%29.pdf

Categories: Expand list: All words cut down to 1 chunk, this list includes plural to color

reference:

Water and sewer: https://www.usgs.gov/special-topic/water-science-school/science/dictionary-water-terms?qt -science_center_objects=0#qt-science_center_objects

https://www.csu.org/CSUDocuments/naturalgasandelectricvocablist.pdf

http://www.geo.mtu.edu/UPSeis/hazards.html

"sewer_and_water": ["discharged", "discharge", "drain", "drainage", "flood", "hygiene", "irrigation", "pipes", "pump", "river", "sanitary", "sewage", "sewer", "stream", "underground", "wash", "waste", "water"]

"power/energy": ["valve", "heat", "gas", "power", "electric", "candle", "flashlight", "generator", "black out", "blackout", "dark", "radiation", "radio rays", "energy", "nuclear", "fuel", "battery", "radiant"]

"roads_and_bridges": ["airport", "avenue", "bridge", "bus", "congestion", "drive", "flight", "jam", "logistic", "metro", "mta", "road", "street", "subway", "traffic", "train", "transit", "transportation", "highway", "route", "lane"]

"medical": ["medical", "red cross", "food", "emergency", "urgent", "evacuate", "evacuating", "evacuation", "protection", "ambulance", "escape", "first aid", "rescue", "rescuing", "dead", "death", "kill", "help", "help out", "help with", "volunteer", "volunteering", "explosion", "exploding", "explode", "victim", "fatalities"]

"buildings": ["collapse", "housing", "house"]

iDataVisualizationLab/VAST2019mc3

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