There are several areas of improvement of the project in the future. The first one is on the completeness of the data. All the above analysis is based on a dataset that reflects a big part of mumbai but not only mumbai. The reason for this is the lack of a way to download a dataset for the entire mumbai without including parts of the neighboring cities. The analyst has to either select a part of the island/city or select a wider area that includes parts of thane and ratnagiri. Also, because of relations between nodes, ways, and relations, the downloaded data expand much further than the actual selection.
As a future improvement, I would download a wider selection or the metro extract from MapZen and filter the non-mumbai nodes and their references. The initial filtering could take place by introducing some latitude/longitude limits in the code to sort out most of the "non-m" nodes.
The second area with room for future improvement is the exploratory analysis of the dataset. Just to mention some of the explorings that could take place:
1.Distribution of commits per contributor.
2.Plotting of element creation per type, per day.
3.Distribution of distance between different types of amenities
4.Popular franchises in the country (fast food, conventional stores, etc.)
5.Selection of a bank based on the average distance you have to walk for an ATM.
6.Which area has the biggest parks and recreation spaces.
The scope of the current project was the wrangling of the dataset, so all the above have been left for future improvement.
Going through this dataset, my concerns were less with the cleanliness of the data - as I found it surprisingly clean - and more with the lack of data. This part of mumbai is too big to have as little information as it does. I think OpenStreetMap can go a long way in developing their map database if they took on certain initiative to increase engagement with their service. One possible initiative would be for OpenStreetMap to form partnerships with educational institutions such as schools, or maybe libraries, to engage students with their service. As a way to develop computer and internet literacy, computer-related courses can teach students how to use OpenStreetMap. It'll expose them to online maps, GPS technology, how to participate in open source projects, and more - all while adding data to a free resource that could benefit the members of the community and the world.
Anticipated Problem: However, the concern here is that you might see an influx of dirty, unreliable data, particularly if the people behind them aren't very computer literate or only participating because it's a mandatory portion of a course. Naturally the data that come from volunteers who get involved because of their genuine passion for the project would be of higher quality.
For data improvement, the biggest problem I came across my data before I cleaned it was the lack of a unified format for street types or phone numbers, or simply incomplete information. If OpenStreetMap had a hard format that street types, phone numbers, zip codes, etc. should follow - and they ensured the format is appropriate for the city/country - there would be much cleaner data for analysis.
Anticipated Problem: The issue here is that you could very likely see a decrease in submissions due to the stricter guidelines.
Finally, open data are here to make average people's life better. For the non-data analyst, it would be nice if there was an application (mobile or web) that could evaluate the suitability of a potential rental house. The work addresses of all family members, importance weights on several amenities like supermarkets, convenience stores, cafes, public transportation, etc. and the application would calculate the suitability of each potential rental. The user would be able to sort them by score and compare them.