Community Asset Ontology for Modeling Community Data using Information Extraction

In this paper, we analyze some data-related challenges to building resilient and sustainable communities, particularly how to computationally model the social and economical dynamic that exists within a community. To that end, we propose the Community Asset Ontology (CAO) for a knowledge graph that can encapsulate community data as modeled in existing social science literature. We utilize existing information extraction paradigms to map natural language community data to CAO and evaluate the usefulness of such an ontology-based approach compared to a baseline open information extraction approach.

Citizenly: A platform to encourage data-driven decision making for the community by the community

As open datasets at the global, national, state and municipal levels become more available, data-driven decision-making is seeing widespread adoption at such levels. But there is a lack of such an approach at the community and neighborhood level. To foster the usage of such data-driven decision-making, we need to encourage community engagement with local data and improve data literacy in communities. Access to a one-stop-shop for all the data generated by the community regarding their neighborhood allows citizens to engage in productive participation. In this paper, we introduce Citizenly, a smart cyber-infrastructure and an easy-to-use application that provides a single access interface for the synthesis of both open datasets and local community data generated by the residents. The goal of Citizenly is to empower the democratization of data science for local community residents to use in applications such as promoting local businesses and services, highlighting issues (for e.g. vacant lots, potholes, etc.) for communities to tackle, and also promote the general use of data in the local community. We discuss the motivation for the problem, research challenges in this approach, and also discuss the qualitative evaluation of the pilot version of the application.

LotRec: A Recommender for Urban Vacant Lot Conversion

Vacant lots are neglected properties in a city that lead to environmental hazards and poor standard of living for the community. Thus, reclaiming vacant lots and putting them to productive use is an important consideration for many cities. Given a large number of vacant lots and resource constraints for conversion, two key questions for a city are (1) whether to convert a vacant lot or not; and (2) what to convert a vacant lot as. We seek to provide computational support to answer these questions. To this end, we identify the determinants of a vacant lot conversion and build a recommender based on those determinants. We evaluate our models on real-world vacant lot datasets from the US cities of Philadelphia,PA and Baltimore, MD. Our results indicate that our recommender yields mean F-measures of (1) 90% in predicting whether a vacant lot should be converted or not within a single city, (2) 91% in predicting what a vacant lot should be converted to, within a single city and, (3) 85% in predicting whether a vacant lot should be converted or not across two cities.