> URBAN INFORMATICS LAB (URBAN INFO LAB)
Many of the social and environmental phenomena have spatial characteristics. Geo-spatial methods, such as, maps, GIS, and etc, are excellent ways to visualize and understand these characteristics. In this page, I will introduce some of the urban analytics and geospatial modelling techniques that I use frequently. Below I showcase selected projects that leverage the power of spatial analytics, web mapping, and geo-visualization.
Noise 311 Research Showcase
One of the tools I frequently use is Leaflet.js. It is a javascript-based web mapping engine that allows for creating a web map with a breeze. Using the Leaflet.js, I created a dot density map and hex map of noise complaints from Vancouver's public 3-1-1 call data in 2016. Here are some of the interactive web mapping results.
Dot Density Map of Noise Complaints in Vancouver, BC from January, 2016 to December, 2016. Use the pull-down menu on the upper right corner of the map to toggle on/off spatial distribution of noise complaints from January to December.
Hex Density Map of Noise Complaints in Vancouver, BC in 2016
Spatial Patterns over Time
For spatial patterns, I put together the noise complaints data for all years from 2010 to 2016. Then, I created a heatmap using a kernel density estimation (KDE) to show clustering patterns of the noise complaints in the City of Vancouver.
Next, I pulled geo-coded information of all the major development projects that exceed 20 million in capital costs from the Province of British Columbia. The data ranges from 2010 to 2016. With the same KDE approach as the noise complaints data, I created a heatmap of the major development projects in the City of Vancouver.
Temporal patterns
With this initial exploration, I explored temporal patterns of noise complaints and major development projects by year. Major development projects are any projects with the estimated capital costs over $20 million CAD. The below graphs show that the total volume of both major development projects and noise complaints have been increasing. The graphs also show that these two data seem to be temporally related.
Both heatmaps show general patterns of noise complaints and major development projects expanding toward southeast direction. Downtown shows the highest clustering patterns for both noise complaints and major development projects.
With these visualizations, I developed a mixed-effects Poisson model estimating the number of nose complaints as a function of the number of major development projects. The panel data required the model to be built using dissemination area as a random clustering factor. Later, I will present the model results and random effects diagnostics as I move forward with the analysis.