A GeoSocial Intelligence Framework for Studying & Promoting Resilience to Seasonal Flooding in Jakarta, Indonesia

PetaJakarta.org is a web-based platform developed to harness the power of social media to gather, sort, and display information about flooding for Jakarta residents in real time. The platform runs on the open source software CogniCity—an OSS platform developed by the SMART Infrastructure Facility, University of Wollongong—which allows data to be collected and disseminated by community members through their location-enabled mobile devices. The project uses a GeoSocial Intelligence Framework to approach the complexity of Jakarta’s entangled hydraulic, hydrological and meteorological systems and thereby converts the noise of social media into knowledge about urban infrastructure and situational conditions related to flooding and inundation. In this paper, PetaJakarta.org co-directors Dr Tomas Holderness, Geomatics Research Fellow at the SMART Infrastructure Facility, Dr Etienne Turpin, Vice-Chancellor’s Postdoctoral Research Fellow at the SMART Infrastructure Facility, and Dr Rohan Wickramasuriyam, GIS Research Fellow at the SMART Infrastructure Facility, will discuss their GeoSocial Intelligence Framework as it applies to their current research in Jakarta. They will also present their preliminary findings from their 2014 Twitter #DataGrant, which has allowed them to develop a correlative analysis between historic social media information, the Jakarta government’s flood maps, and the infrastructure used to manage critical flood emergencies. Finally, they will speculate on several future applications of the CogniCity OSS and suggest how it might be developed to further promote an integrated civic co-management platform with the support of business, industry, government and community organizations

Incorporating citizen science:enhancing hydrological modeling through crowdsourcing

Abstract. General public participating in research design, data collection or analysis process is often referred to as citizen science, and when digital means are involved, it’s defined as crowdsourcing. This thesis project is aimed at examining the feasibility and potential of using citizen science/crowdsourcing for hydrological modelling. The research project revolves around developing a user friendly crowdsourcing mobile application for gathering data from the citizens, which will be specific to urban flooding data, river ice data, lake water quality data and vegetation condition data. The registered users are able to register on the application and upload data in the form of reports, which will be in text form and also attach images of the situation. In the end, we utilize the text reports uploaded by users regarding urban flooding to extract useful hydrological insights, that could be used for updating already existing hydrological models as well as create new hydrological models using NLP. The results indicate that it is possible to extract useful insights from the data reports submitted by the citizen scientists, which could be further used for updating hydrological models or maybe set alerts for the hydrologists in case of important hydrological updates

Understanding citizen science and environmental monitoring: final report on behalf of UK Environmental Observation Framework

Citizen science can broadly be defined as the involvement of volunteers in science. Over the past decade there has been a rapid increase in the number of citizen science initiatives. The breadth of environmental-based citizen science is immense. Citizen scientists have surveyed for and monitored a broad range of taxa, and also contributed data on weather and habitats reflecting an increase in engagement with a diverse range of observational science. Citizen science has taken many varied approaches from citizen-led (co-created) projects with local community groups to, more commonly, scientist-led mass participation initiatives that are open to all sectors of society. Citizen science provides an indispensable means of combining environmental research with environmental education and wildlife recording. Here we provide a synthesis of extant citizen science projects using a novel cross-cutting approach to objectively assess understanding of citizen science and environmental monitoring including: 1. Brief overview of knowledge on the motivations of volunteers. 2. Semi-systematic review of environmental citizen science projects in order to understand the variety of extant citizen science projects. 3. Collation of detailed case studies on a selection of projects to complement the semi-systematic review. 4. Structured interviews with users of citizen science and environmental monitoring data focussing on policy, in order to more fully understand how citizen science can fit into policy needs. 5. Review of technology in citizen science and an exploration of future opportunities

A strategic framework to support the implementation of citizen science for environmental monitoring. Final report to SEPA

In this report we provide a decision framework that can be used to guide whether and when to use a citizen science approach for environmental monitoring. Before using the decision framework we recommend that five precursors to a citizen science approach are considered

Harnessing the power of the general public for crowdsourced business intelligence: a survey

International audienceCrowdsourced business intelligence (CrowdBI), which leverages the crowdsourced user-generated data to extract useful knowledge about business and create marketing intelligence to excel in the business environment, has become a surging research topic in recent years. Compared with the traditional business intelligence that is based on the firm-owned data and survey data, CrowdBI faces numerous unique issues, such as customer behavior analysis, brand tracking, and product improvement, demand forecasting and trend analysis, competitive intelligence, business popularity analysis and site recommendation, and urban commercial analysis. This paper first characterizes the concept model and unique features and presents a generic framework for CrowdBI. It also investigates novel application areas as well as the key challenges and techniques of CrowdBI. Furthermore, we make discussions about the future research directions of CrowdBI

Spatial and Temporal Sentiment Analysis of Twitter data

The public have used Twitter world wide for expressing opinions. This study focuses on spatio-temporal variation of georeferenced Tweets’ sentiment polarity, with a view to understanding how opinions evolve on Twitter over space and time and across communities of users. More specifically, the question this study tested is whether sentiment polarity on Twitter exhibits specific time-location patterns. The aim of the study is to investigate the spatial and temporal distribution of georeferenced Twitter sentiment polarity within the area of 1 km buffer around the Curtin Bentley campus boundary in Perth, Western Australia. Tweets posted in campus were assigned into six spatial zones and four time zones. A sentiment analysis was then conducted for each zone using the sentiment analyser tool in the Starlight Visual Information System software. The Feature Manipulation Engine was employed to convert non-spatial files into spatial and temporal feature class. The spatial and temporal distribution of Twitter sentiment polarity patterns over space and time was mapped using Geographic Information Systems (GIS). Some interesting results were identified. For example, the highest percentage of positive Tweets occurred in the social science area, while science and engineering and dormitory areas had the highest percentage of negative postings. The number of negative Tweets increases in the library and science and engineering areas as the end of the semester approaches, reaching a peak around an exam period, while the percentage of negative Tweets drops at the end of the semester in the entertainment and sport and dormitory area. This study will provide some insights into understanding students and staff ’s sentiment variation on Twitter, which could be useful for university teaching and learning management