Development of city districts in US has been highly inequitable by socio-economical standards , very much by the recent protests across the major cities in country . the root cause of issues cant only be economic disparity , but combination of disparities ranging in diffrent domains ( like facilities for health , education , housing and other basic infrastructure of institutions ).

Given the fact that city governments have huge budgets curating data and statistics about the socio-economic parameters , there has been less emphasis on implementing the right platform / application for giving the shareholders ( the people living in communities which are specially disadvantaged ) more information driven graphical analysis about their area's infrastructure . thus leaving the onus to the companies and entrepreneurs , most of which , are solely focused on fuelling further building the applications to implement gig economies which are mostly counterprouctive , rather than appraising the people with the fact based decision making for investments in social infrastructure , most of which have only solved the issue of employment but still the inefficiencies of government for resource allocation in social infrastructure and amenities . thus giving people more data driven stats in order to get them participate in decision making process .

What it does :

It integrates the diffrent datasets ( clubbed by diffrent sectors of society like academics , health , administrative , economic ) to the HERE Maps / data layers in order to show the statistics based on the diffrent topics and their corresponding datasets in each district . for instance getting the information about the diffrent schools in the preccinct , statistics about the students academic performance , investment in the social infrastructure , etc.

How we built it

  1. For frontend , we used the frontend react template from flatlogic and did the changes to make it simple for user , in both

  2. on backend , we integrated the api with the HERE data studio , which houses all the layers we considered along with our custom dataset from OPEN-NYC.

  3. and the application will soon be deployed on serverless architecture .

Challenges we ran into

  1. Non standardised data : we needed to clean the data in order to effectively geoincode and display

  2. Quantity of data : most of the data is very less , some of them just dozen of rows and few parameters , thus we had to normalise them with other corresponding datasets from

  3. understanding the

Accomplishments that we're proud of

  1. creating an immersing UI for allowing people to visualise datasets

  2. collborating as an team from diffrent backgrounds and meeting with each other to develop an application for promoting an

What we learned

  1. the immensive ecosystem of HERE to provide complete stack of tools to esthablish standalone application for making real time predictive maps for the smart cities or data driven analysis in our case

  2. the immense potential to decentralise the development of the Map based applications to understand the evolution of several challanges being faced by the society by using maps based applications .

What's next for Localease : exploring data driven maps for CSR oppertunities:

  1. extend to other cities of the US , which have credible datasets .

  2. integrating the machine learning models to predict the patterns of statistics : thanks to Our Collague Mr Mohammed Rahemi , we did analysis of certain statistics , like house prediction market in toronto.

  3. integration with the platform for participative democracies ( like Decidim) : Decidim is an open source SaaS framework for managing the governance of diffrent initiatives ( decisions , resolutions , budgeting etc).

  4. integrate an notebook based envornment ( using Idyll.js ) . in order to create data driven journalistic news to give an better understanding to the city officials and dwellers alike .

Share this project: