Background

Smart city is defined by IBM at first as using modern technologies to collect, process, and integrate the key information of core systems in running cities. Institute for Management Development(IMD) and Singapore University of Technology and Design (SUTD) establish a smart city index that focuses on both economic and technical elements of smart cities, as well as "humane features" (quality of life, environment, inclusion) of smart cities.

This is a project to help an expert in urban design to understand smart city data. The data is coming from the There are two types of data to understand. One is the correlation data that shows the correlation between indicators. The other is the performance data that shows the score of different indicators for each city. We want to explore how node-edge visual representation can help analysis understand the data.

Approach

We use plotly Dash, a Python package to build the tool. There are two different visualizations, one is the performance graph and the other one is the correlation graph. We allow analysts to interact with both visualizations in different ways.

For the performance graph, nodes represent cities and indicators while edges represent the performance value. A wider edge means a higher performance value. We use the graph to compare cities in different regions to obtain insights about how regions are varied from others. Users allow to choose regions, cities, category of indicators, and indicators under the category to generate graphs.

For the correlation graph, nodes represent indicators and edges represent their correlations. Solid edges means the connected indicators are positively correlated to each other while dash edges represent a negative correlation. Similarly, a wider edge reveals a higher correlation. There are also multiple operations being supported including seleting indicators, setting correlation ranges, and inspect specific indicator.

Video

Teams