Development of public dynamic spatio-temporal monitoring and analysis tool of supply chain vulnerability, resilience, and sustainability

Supply chains play a pivotal role in driving economic growth and societal well-being, facilitating the efficient movement of goods from producers to consumers. However, the increasing frequency of disruptions caused by geopolitical events, pandemics, natural disasters, and shifts in commerce poses significant challenges to supply chain resilience. This draft update report discusses the development of a dynamic spatio-temporal monitoring and analysis tool to assess supply chain vulnerability, resilience, and sustainability. Leveraging news data, macroeconomic metrics, inbound cargo data (for sectors in California), and operational conditions of California’s highways, the tool employs Natural Language Processing (NLP) and empirical regression analyses to identify emerging trends and extract valuable information about disruptions to inform decision-making. Key features of the tool include sentiment analysis of news articles, topic classification, visualization of geographic locations, and ..., The research team implemented a two-stage procedure to streamline the collection, processing, and analysis of news data. The stages are as follows:

Lexicon Setup: This stage establishes the lexicons required for sentiment and topic analysis. Topics are categorized into eight groups relevant to supply chain risks: political, environmental, financial, supply and demand, logistics, system, infrastructure, and sector. Sentiments are evaluated using a dictionary-based approach with the AFINN lexicon.Three comprehensive lists of countries, states, and cities are used to classify geographical locations at three hierarchical levels: countries, states within the U.S., and cities/municipalities within California.

News collection and processing: Automated algorithms collect the most recent news daily, with a 24-hour lag, based on the predefined query: (USA or United States) and (supply chain or supply-chain) and (disruption or resilience) and (retailer or warehouse or transportation or factory)..., , # News indicators on Supply Chain Vulnerability, Resilience, and Sustainability

[https://doi.org/10.5061/dryad.qjq2bvqqj](https://doi.org/10.5061/dryad.qjq2bvqqj)

This dataset presents the key features extracted from supply-chain-related news articles. The news articles are gathered based on the following query: (USA or United States) and (supply chain or supply-chain) and (disruption or resilience) and (retailer or warehouse or transportation or factory). The features are extracted using Natural Language Processing (NLP) techniques and include:

1. Term frequencies and Term Frequency-Inverse Document Frequency (TF-IDF). Term frequencies and Term Frequency-Inverse Document Frequency (TF-IDF) are calculated at unigram and bigram levels. TF-IDF is a widely used metric for measuring the relationship and relevance of words in documents, where tokens with higher TF-IDF values are considered more representative.
2. Topic share. News articles are classified into eight topics relevant to supp...