Working Paper by Mark S. Fox (University of Toronto): “Cities are moving towards policymaking based on data. They are publishing data using Open Data standards, linking data from disparate sources, allowing the crowd to update their data with Smart Phone Apps that use Open APIs, and applying “Big Data” Techniques to discover relationships that lead to greater efficiencies.
One Big City Data example is from New York City (Schönberger & Cukier, 2013). Building owners were illegally converting their buildings into rooming houses that contained 10 times the number people they were designed for. These buildings posed a number of problems, including fire hazards, drugs, crime, disease and pest infestations. There are over 900,000 properties in New York City and only 200 inspectors who received over 25,000 illegal conversion complaints per year. The challenge was to distinguish nuisance complaints from those worth investigating where current methods were resulting in only 13% of the inspections resulting in vacate orders.
New York’s Analytics team created a dataset that combined data from 19 agencies including buildings, preservation, police, fire, tax, and building permits. By combining data analysis with expertise gleaned from inspectors (e.g., buildings that recently received a building permit were less likely to be a problem as they were being well maintained), the team was able to develop a rating system for complaints. Based on their analysis of this data, they were able to rate complaints such that in 70% of their visits, inspectors issued vacate orders; a fivefold increase in efficiency…
This paper provides an introduction to the concepts that underlie Big City Data. It explains the concepts of Open, Unified, Linked and Grounded data that lie at the heart of the Semantic Web. It then builds on this by discussing Data Analytics, which includes Statistics, Pattern Recognition and Machine Learning. Finally we discuss Big Data as the extension of Data Analytics to the Cloud where massive amounts of computing power and storage are available for processing large data sets. We use city data to illustrate each.”
Analyzing the Analyzers
An Introspective Survey of Data Scientists and Their Work,By Harlan Harris, Sean Murphy, Marck Vaisman: “There has been intense excitement in recent years around activities labeled “data science,” “big data,” and “analytics.” However, the lack of clarity around these terms and, particularly, around the skill sets and capabilities of their practitioners has led to inefficient communication between “data scientists” and the organizations requiring their services. This lack of clarity has frequently led to missed opportunities. To address this issue, we surveyed several hundred practitioners via the Web to explore the varieties of skills, experiences, and viewpoints in the emerging data science community.We used dimensionality reduction techniques to divide potential data scientists into five categories based on their self-ranked skill sets (Statistics, Math/Operations Research, Business, Programming, and Machine Learning/Big Data), and four categories based on their self-identification (Data Researchers, Data Businesspeople, Data Engineers, and Data Creatives). Further examining the respondents based on their division into these categories provided additional insights into the types of professional activities, educational background, and even scale of data used by different types of Data Scientists.
In this report, we combine our results with insights and data from others to provide a better understanding of the diversity of practitioners, and to argue for the value of clearer communication around roles, teams, and careers.”
OGP Report: "Opening Government"
Open Gov Blog: “In 2011, the Transparency and Accountability Initiative (T/AI) published “Opening Government” – a guide for civil society organisations, and governments, to support them to develop and update ambitious and targeted action plans for the Open Government Partnership.
This year, T/AI is working with a number of expert organisations and participants in the Open Government Partnership to update and expand the guide into a richer online resource, which will include new topic areas and more lessons and updates from ongoing experience….
Below you’ll find an early draft of the section in GoogleDocs, where we invite you to edit and comment on it and help to develop it further. In particular, we’d value your thoughts on the following:
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Are the headline illustrative commitments realistic and stretching at each of the levels? If not, please suggest how they should be changed.
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Are there any significant gaps in the illustrative commitments? Please suggest any additional commitments you feel should be included – and better yet, write it!
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Are the recommendations clear and useful? Please suggest any alterations you feel should be made.
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Are there particular country experiences that should be expanded on? Please suggest any good examples you are aware of (preferably linking to a write-up of the project).
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Are there any particularly useful resources missing? If so, please point us towards them.
This draft – which is very much a work in progress – is open for comments and edits, so please contribute as you wish. You can also send any thoughts to me via: tim@involve.org.uk”
Policy Modeling through Collaboration and Simulation
New paper on “Bridging narrative scenario texts and formal policy modeling through conceptual policy modeling” in Artificial Intelligence and Law.
Abstract: “Engaging stakeholders in policy making and supporting policy development with advanced information and communication technologies including policy simulation is currently high on the agenda of research. In order to involve stakeholders in providing their input to policy modeling via online means, simple techniques need to be employed such as scenario technique. Scenarios enable stakeholders to express their views in narrative text. At the other end of policy development, a frequently used approach to policy modeling is agent-based simulation. So far, effective support to transform narrative text input to formal simulation statements is not widely available. In this paper, we present a novel approach to support the transformation of narrative texts via conceptual modeling into formal simulation models. The approach also stores provenance information which is conveyed via annotations of texts to the conceptual model and further on to the simulation model. This way, traceability of information is provided, which contributes to better understanding and transparency, and therewith enables stakeholders and policy modelers to return to the sources that informed the conceptual and simulation model.”