Artificial intelligence and big data to use for vulnerability mapping : in case of Delhi

Abstract

Keywords: Big Data, Artificial Intelligence, COVID-19, Vulnerability Index The motivation behind this study is to pave the way for successful use of the available resources that the Artificial Intelligence and Big Data analytics have today to predict, understand and monitor disaster scenarios. Most affected countries by Natural Disasters are India (805m), Thailand (76m), China (2,274m), Pakistan (55m), Kenya (47m), and Philippines (130m) etc. are the top 6 countries with the highest absolute number of affected people (in million)(UNISDR- 2015). In these disasters, human suffering and economic damages are recorded as highest. 184 million people (approx.) are displaced by natural disasters exceeding the global economy by $300 billion (annually) from 2008 – 2014 as per records. Organizations use big data for the sole purpose of analytics. However, before businesses can begin extracting ideas and useful information from big data, they should be familiar with a variety of big data outlets. Data, as we all know, is vast and comes in many ways and ends up wasting time and money if not categorized or sourced properly. To be effective in big data, businesses must be able to sort through the different data sources available and identify them according to their usability and importance. Social media or journalism forms a base for big data source as it yields Images, videos, audios, podcast etc. These provide with useful insights into customer habits and the changing patterns. It is the quickest way for companies to get an indepth analysis of their target audiences to draw trends as it is self-broadcast and crosses both physical, demographical barriers providing conclusions and enhancing their decision-making. Use of real time data reduces community harm and generates immediate responses for continued parallel work. The thesis aims in Integrating Artificial Intelligence and Geospatial Big Data with Spatial data infrastructure to create Social media-based COVID-19 Vulnerability Mapping. The objective of the research is to identify available Open Geospatial Consortium (OGC) and Application programming interface of social media platforms for data mining. For assessing the COVID- 19 vulnerability index with multi criteria parametric analytics, parameters for COVID -19 are identified through case studies and researches available. The primary survey yields the on ground data covering the Base Map, Population Density, Dilapidated Residences, etc. Further, the volunteered geographic information with conventional mapping techniques identified on the problems related to COVID 19 vulnerable area is interpolated with the real time data, to solve the problem in the real time scenario. To achieve the solutions for aforementioned objectives, a proposal is devised to develop framework creating spatial data infrastructure for better real time decision making which further will lead to create different frameworks for different zones to resolve problems like health and vaccinations in the area through real time data base.