Helping home buyers know the factors that are making an impact on a state.
A home buyer weighs multiple factors in the process of buying a home. Firstly, the location of the property is important. (For the purposes of this project, a location refers to a State of )On top of the location, knowing the general stability of a location, and the people and groups that support or challenge that stability, is also valuable.
Link: infocurrent.xyz
The solution for the business problem can be solved by compiling and looking at the news stories from a certain location. In general, this can in general give the answer that the user is looking for.
InfoCurrent filters through events logged in the GDELT database to produce results for the user. InfoCurrent in particular only considers events in US States. The data in GDELT does hold some sparse data, and part of the process within InfoCurrent, is to filter out sparse data and extract the useful data. Events consist of many different points of information, and one that's crucial for this application is the 'Goldstein Scale' of an event. The scale falls between -10.0 to 10.0, severely negative impact by extreme conflict to post impact by extreme cooperation. This rating itself is determined by the type of event it is addressing.
The events for the US states are consolidated and grouped by year. The number of events happening in a state are tracked, and the goldstein ratings are continously summed. Using the sum of the goldstein ratings and the number of events from a location, the average of the goldstein rating can be quickly derived. Note that, the average goldstein rating also serves as the final "Impact score" that the user sees. Also note that the rating is normalized to a value between 0.0-1.0 before the final step.
The GDELT project collects news stories from print and web sources from around the world. It's able to identify a number of people, organizations, themes, emotions, and ultimately events that are driving the global soceity. This live data mining projects produces one of the largest open spatiotemporal datasets that exist.
New GDELT updates are acquired from the source. A Python script processes the data and places it in a PostgreSQL database. Since GDELT updates are posted every 15 minutes, an Airflow workflow is scheduled to complete this process as new data arrives.
GDELT's historic data exists in an Amazon S3 bucket. An offline batch processing Apache Spark job reads and processes the data from S3. The processed data is saved in a PostgreSQL database. The user facing component of this pipeline is the Flask application. The user is able to specify a single state, and a set of actors they are interested in. The application makes the appropriate queries to the PostgreSQL database. The results are viewed on the Flask application.
Link to Flask application: infocurrent.xyz
Things are need to be installed and running
- Apache Spark
- PostgreSQL Database
- Flask
- Airflow
- Python
- sqlalchemy
- pandas
- psycopg2
- pandasql
Link to Infocurrent presentation
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