Uploaded on 2016-09-01 by Abhishek Shinde

PRINCIPLES FOR THE CITIZEN DESIGN SCIENCE PROJECT- The emergence of Citizen design science as an radical thinking process for Urban Design has triggered the use of citizen behaviors ,local knowledge,immense contribution to the crowd sourcing data through use of social media like Whatsapp,Facebook,Twitter,etc.Open data dashboard initiatives are changing not only the relationship between government and the public, but also the relationships between different business units within government responsible for delivering the services being measured. Based on the Module 10 understanding and the additional readings , frameworks of various Citizen science project from developing countries like NYC DataMine,London City Dashboard and CityEye-Realtime dashboard for mapping mobility ,MatSim and Simulacra ,M-Atlas system will serve as crucial parameters for framing the citizen Design Science project In my resident locality Kasarvadavli,Thane,Maharshtra,India.Kasarvadli is flanked by the Ghodbunder road which connects two major areas Borivali and Thane. Also,Few of the Recent Citizen science projects in India like treemetastudio and MigrantWatch are informative. In brief,MigrantWatch depends entirely on the enthusiasm and dedication of the volunteers who look for migrants and contribute this information.While,Treemetastudio documents the trees in the cities using volunteers who plot the trees on the Google maps. Thane city lacks proper infrastructure,has poor mobility due to disorganized bus services and auto rickshaws .The major problem faced by the residents abutting the Ghodbunder road is Contrary,recently car pooling schemes have been recently started by OLA,NEHRU Cabs from Borivali station to Thane station which could serve for crowdsourcing data collection.Also,the road is undergoing reconstruction for the construction of the Mumbai metro Line nos4 which will run from Wadala-Ghatkopar-Teen Hath Naka (Thane)- Kasarwadavli corridor length and is 32 Km of which it is 27 Km. Underground and 5 Km. Elevated. Based on the existing scenarios ,following are the guidelines to start a Citizen science project I. Understanding the intended Netizen User Citizen Awareness and Promoting Public Participation,Appreciation-Engage the public in the processes of urban maintenance, creating appreciation of these often-invisible services. Crowd sourced data are generated on a three tier process-a set of intellectual mindset citizens ,Information technology companies like Microsoft,IBM,CISCO,Accenture,etc., Participation and self-organisation are the cornerstones to building a global knowledge resource that, by design, will represent a public good, accessible to every citizen, institution or business.Only a public system capable of delivering high-quality information within a trusted framework has the potential for raising a high degree of participation, and only large, democratic participation can ensure the creation of reliable, timely and trustworthy information about collective phenomena. II. Framework for Crowd sourcing Data collection based on the locality -Conservation of Local biodiversity,bringing together urban services and the citizens through real-time information dashboards, and by integrating user activated feedback loops through their mobile devices,Creating Web based platforms for micro blogging about complaints to the Municipalities and Governing Bodies via photographs,Linking Twitter and complaint portals to the Right to Information Act portal,Ease down the Tedious process of applying RTI for different Urban issues like Potholes,Electricity failures,Lack of Water Supply and Garbage disposal using Real time Asset Managements via GPS (Global positioning systems)with which could document all of the urban issues on a single platform and make it visually available to citizens on their smartphones,Mapping Real time Weather, Air Quality ,Urban local climates,CO2 emissions Energy demand via Suitable programs like ARCGIS,Sentilo or Santender program, with numerical values in color coded boxes for ease of view.Documenting Livability,Happiness quotient,Employment rate via mapping the location of Commercial zones ,offices and the employee's residence on Google Maps or Open Street Maps ,thereby understanding travel time,job satisfaction level .Use of RFID scans and Bluetooth radios that count the number of active mobile devices carried by pedestrians , a proven proxy for overall pedestrian flow.Social and economic indicators, such as unemployment Rate, gross domestic product (GDP), gross national product (GNP), balance of payments, inflation, and the consumer price index (CPI)must be made visible to the common citizens. III. Filter and Editable,Real time,Transparency,Coupling and Trajectory,Bench-marking and Monitoring -Users can then filter the data and access it in raw form.Use of simplifies key performance indicators (KPIs), interaction design, and information selection criteria,combination of simplified KPIs, a clear explanation of what each one represents, and displays them in a clear graphical form that uses consistent color-coding, iconography and fonts.Historical data analysis engine for decision making planning purposes,managing service routes and schedules .Indirect sentiment analysis, gauging citizen satisfaction with public spaces, which can also guide near and long-term decisions on where to send services and how to incorporate and integrate citizens feedback. Deploy incident reports or requests, which become publicly visible. Use of City benchmarking which consists of comparing urban indicators within and across cities to establish how well an area/city is performing vis-à-vis other locales or against best practice. The process is often accompanied with score carding (Kaplan & Norton, 1992).Salient aspects of individual daily routines, such as the most frequently visited cells, and the time and periodicity of such stays. Therefore, these data help us scrutinize the spatial patterns together with social structure and the intensity of social interactions.Much of this data is networked and we consider that coupling such networks of data bases will be key to making sense of this material. establish standards for integration of this data, for ensuring that quality standards are met, for assessing the accuracy and error in such data, and for providing ways of filling in missing data using models of the very systems that this data pertains to.Linking GPS, satellite remote-sensing, online interactive data systems focused on crowd-sourcing, all with the automation of standard secondary sources of data, and then meshing this with more unconventional data elicited from social media provides a very rich nexus of possibilities in terms of providing new and open sources of data essential to a better understanding of how smart cities will function. IV. UserInterface-Users must be more engaged when they are actively involved in the tutorial and not just reading text.Start users off with a basic contribution task and allow experienced volunteers to take on more advanced tasks.Remind users why their contribution is important, particularly when a task is long or tedious. Also when aspects of a task aren’t obviously related to the project’s goals, explain why this part of the task is necessary.Keep users informed about their personal progress and the project’s progress. Utilize progress bars (e.g. you have completed 2 out of 4 steps), counters (e.g. you have contributed 6 photos), and project blogs. Gamification mechanisms, such as badges and narratives, might also be worth considering (Bowser et al. 2013).n Kloezter et al. (2013) we identified 3 levels of learning: the project (i.e. the science behind the project), the task (i.e. the task mechanics), and the community (i.e. peer-to-peer learning). Sometimes designers forget the third level – the community. This is unfortunate because we have found that a sense of community is linked to high levels of engagement and creativity (Jennett et al., 2013). It’s important to provide volunteers with ways to interact with each other, such as forums, chat, and even social media (e.g. Facebook). V. Challenges and Urban Governance-The challenges in crowdsourcing data and its management include - Appraisal of the validity, veracity and fidelity of data. Use of High end precision Sensors ,Actuators ,GIS,LIDAR technologies and also managing ,analyzing,sorting the data with low computing capabilities data centers leads to high cost for data generation. New managerial-ism and contextual policy formulation which form key elements in the move towards data-driven, evidence-based governance and policy formulation. Very few citizens in Thane are aware of the Citizen Design Science and also accessibility of data collection to every individual in the locality is not confirmed,leading to incomplete data. New methods of coupling in terms of hardware and software will be needed and this will be central to the sort of collective intelligence functions that we see the smart city developing. Quality data generation and its assessment using a standard protocols is also one challenge.- Brute force risk assessment of a system as complex as a metropolitan area is beyond the current computing abilities of even the highest speed clusters: the number and types of agents are too large and the time to enable such computation too long.Developing and coupling databases which in turn are being forged using new kinds of media for collecting data through sensing, mining online transactions, and the automated recording of behavior in the environment and communication are also a big challenge. The attached image of Citizen Design Science project by Treemetastudio which map trees in Mumbai along the streets along with their characteristics