Android App, Collect GPS-based Location in the Cloud with Fusion Tables and Map

This is a very simple Android app with Google Fusion Tables and Map, in which user can submit location coordinates based on GPS/other location. Location(s) is saved in Fusion Tables, it can then be displayed in map. 

Fusion Tables serves cloud-based table as data persistence, in this example data is collected from Android device. 

I shall create a basic Android app in MIT App Inventor 2 for learning purpose. This basic app can then be used as part of a more complex Android app.  😇 

K-means Clustering: Geolocation EDA & Inference Using Cell-phone CDRs

I played with (real) CDRs data, doing some exploratory data analysis / EDA and inferential (clustering), in order to find out approximate geolocation of certain phone number. 

Sometimes we're curious e.g. "who is this woman?"
Does she work in a bank, or studio? Maybe she's a bus driver? ;)

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Data science + machine learning tools: Python (pandas, sklearn, matplotlib).

Call Detail Records (CDRs) is cell phone usage information collected by cell phone service providers.
Providers record every voice call or text (SMS) message exchange between two cell phone numbers. Information collected includes:

• calls and messages: phone number, reciprocal phone number, time, duration, length of messages, etc., relational to users' identity.
• cell towers: transceiver station CID, LAC (location area code), etc., relational to latitude and longitude data.
• devices: IMEI, MAC, name and type of device, etc.
• wifis: name, MAC of base station, etc.
• ... and other data for resource provisioning and billing.

It's a comprehensive data, and it's available almost in real-time (within minutes). 
We can also do similar EDA to social media's geotagged data.