On Road Safety Management of Hazardous Materials using Exploratory & Network analysis of GeoData using Python tools

It was almost 15 years since I worked on multi-objective Air Quality Monitoring site selection using Geographic Information System. At that time use of ArcGIS / Python was in nascent stage in ESRI product. We were using python as a scripting languages with use around some minor level of automation. Now it has matured a lot with advanced machine learning and Deep Learning capabilities which can be used to build sophisticated deep learning analysis for network and land use usages. In this blog I am trying to give an overview of how best such technique can be used to manage the Hazardous Material Transport for an organization.

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A Data Science Approach for effective Management of Process Safety Incidents

As mentioned in the CCPS Process Safety Guide, an essential element of any improvement program is measure of existing and future performance. The proper analysis of such measures like Leading and Lagging metrices is critical for successful process safety management. Generally, a safety pyramid consists of mix of three types of metrices like Lagging metrices – which are retrospective set of measures, Leading metrices – which are forward looking metrices and finally Near-miss which are less severe incidents, which however are very good indicators for future likelihood. Indeed, the availability of data science analysis tools along with enterprise systems is very critical for mitigating catastrophic chemical accidents.

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