Mining Social Media and Structured Data in Urban Environmental Management to Develop Smart Cities

This research presented the deployment of data mining on social media and structured data in urban studies. We analyzed urban relocation, air quality and traffic parameters on multicity data as early work. We applied the data mining techniques of association rules, clustering and classification on urban legislative history. Results showed that data mining could produce meaningful knowledge to support urban management. We treated ordinances (local laws) and the tweets about them as indicators to assess urban policy and public opinion. Hence, we conducted ordinance and tweet mining including sentiment analysis of tweets. This part of the study focused on NYC with a goal of assessing how well it heads towards a smart city. We built domain-specific knowledge bases according to widely accepted smart city characteristics, incorporating commonsense knowledge sources for ordinance-tweet mapping. We developed decision support tools on multiple platforms using the knowledge discovered to guide urban management. Our research is a concrete step in harnessing the power of data mining in urban studies to enhance smart city development

Commonsense Knowledge in Sentiment Analysis of Ordinance Reactions for Smart Governance

Smart Governance is an emerging research area which has attracted scientific as well as policy interests, and aims to improve collaboration between government and citizens, as well as other stakeholders. Our project aims to enable lawmakers to incorporate data driven decision making in enacting ordinances. Our first objective is to create a mechanism for mapping ordinances (local laws) and tweets to Smart City Characteristics (SCC). The use of SCC has allowed us to create a mapping between a huge number of ordinances and tweets, and the use of Commonsense Knowledge (CSK) has allowed us to utilize human judgment in mapping. We have then enhanced the mapping technique to link multiple tweets to SCC. In order to promote transparency in government through increased public participation, we have conducted sentiment analysis of tweets in order to evaluate the opinion of the public with respect to ordinances passed in a particular region. Our final objective is to develop a mapping algorithm in order to directly relate ordinances to tweets. In order to fulfill this objective, we have developed a mapping technique known as TOLCS (Tweets Ordinance Linkage by Commonsense and Semantics). This technique uses pragmatic aspects in Commonsense Knowledge as well as semantic aspects by domain knowledge. By reducing the sample space of big data to be processed, this method represents an efficient way to accomplish this task. The ultimate goal of the project is to see how closely a given region is heading towards the concept of Smart City

Gamification: Stimulating User Smart City Application

The urban problems have led to an increase in the population in urban areas considering the needs of many government employees to provide service to all citizens. However, in actual conditions, government employees are limited, therefore, need community empowerment involved to support government management of the city. The solution for community empowerment is using communication technology as a public space for the citizens to communicate with the government. Citizen participation used smart city application ass public space to inform the sentiment public to the government. Citizens have adapted from traditional public play to public technological space. Gamification on smart city application devices motivates citizens, which has a growing influence on reporting and complaint activity. Technology set up gamification to create active citizens by reporting complaints of city infrastructure. Results identified gamification encourage a lot of citizen participation in reporting sentiment and complaint inside smart city applications

Machine Learning Approaches in Agile Manufacturing with Recycled Materials for Sustainability

It is important to develop sustainable processes in materials science and manufacturing that are environmentally friendly. AI can play a significant role in decision support here as evident from our earlier research leading to tools developed using our proposed machine learning based approaches. Such tools served the purpose of computational estimation and expert systems. This research addresses environmental sustainability in materials science via decision support in agile manufacturing using recycled and reclaimed materials. It is a safe and responsible way to turn a specific waste stream to value-added products. We propose to use data-driven methods in AI by applying machine learning models for predictive analysis to guide decision support in manufacturing. This includes harnessing artificial neural networks to study parameters affecting heat treatment of materials and impacts on their properties; deep learning via advances such as convolutional neural networks to explore grain size detection; and other classifiers such as Random Forests to analyze phrase fraction detection. Results with all these methods seem promising to embark on further work, e.g. ANN yields accuracy around 90\% for predicting micro-structure development as per quench tempering, a heat treatment process. Future work entails several challenges: investigating various computer vision models (VGG, ResNet etc.) to find optimal accuracy, efficiency and robustness adequate for sustainable processes; creating domain-specific tools using machine learning for decision support in agile manufacturing; and assessing impacts on sustainability with metrics incorporating the appropriate use of recycled materials as well as the effectiveness of developed products. Our work makes impacts on green technology for smart manufacturing, and is motivated by related work in the highly interesting realm of AI for materials science

Memos and Mega Projects: Applying Planners’ Perceptions of Their Software to a Framework for the Future of Planning

Software powers the modern urban planning department. However, the majority of academic attention on software in the planning profession has focused on highly specialized land use models, ignoring the importance of common applications that most planners rely upon throughout their workdays. For example, email’s impact on planning has gone largely undiscussed in the literature despite its role as one of the most commonly used software by planners. This report has a twofold purpose: 1) create a protocol for interviewing planners about the software they use routinely; 2) synthesize needs and expectations of planners gathered during interviews with relevant literature on planning technologies into a framework for the future of planning software. The framework presented in this report unifies, for the first time, disparate fields of research on software related to urban planning into a single set of guidelines for developing the future of software for public agencies. This framework provides a research agenda for urban planning software systems that mutually strengthen one another, and a valuable conceptual overview of the diverse information systems involved in the planning profession. Eleven interviews were conducted with mid- and senior-level planners in local governments across Santa Clara County, better known around the world as Silicon Valley. Santa Clara County was selected as the study area for two reasons: well-resourced governments in the area can invest in modern planning software, and to question if the stereotype of the area’s technological leadership extends to its local governments. Senior-level planners were interviewed in a semi-structured format with the interview adjusted based on a short survey about the software most used in the individual’s professional role. Key findings from the interviews informing the framework include: Planners in local governments in Silicon Valley are transitioning into modern software tools, like electronic plan review and permit management systems. There is no special technological advantage in Silicon Valley among public agencies. Planners were eager to fully implement and adopt software features available to them, particularly features that would improve communication about project status with applicants; Planners were unafraid of software automation. Limited automation features available in electronic plan review systems were yet to be fully implemented, and planners embraced the time-saving potential; The volume of email burdened interviewees. This draws attention to the significance of generalized productivity software in the practice of planning; Planners had no immediate need for “big data,” despite the recognized importance of big data in the urban planning technology literature. Perceptions from planners about the software that they use informed key problems and set goals for the framework developed here. Extensive research into emerging software targeting the construction and engineering trades with relevance to planners, as well as software designed to assist creative knowledge workers, informed the development of the future framework for planning software. Features of the framework include: A planning data model that underpins land use codes, development guidelines, and planning department procedures, providing machine-readable logic that underpins rulebased systems in email, project tracking, permit management, electronic plan review, and staff reports; Template-based and data type-aware word processing that encodes standardized practices for writing documents and requires numeric data be stored and represented as such. Electronic plan review systems that assist in checking both objective zoning codes and subjective design guidelines using generalized adaptable rule language; Integrated BIM-GIS supporting both the plan review and permit management process by organizing and visualizing spatial and physical data about the built environment; and Predictable, structured times to respond to email from applicants and the public and process-integrated calendars that recover time for focusing on long-term planning efforts; The generalized productivity software that planners have been using for over thirty years is inadequate for the predicted era of big data generated by networked urban environments. Excel is not designed to support real-time analytics, Word is not designed to assist in describing or associating analytics with textual information, and no application has yet been designed to visualize or organize such data for engaging the public. This framework gives planners and researchers of planning technology insight into the range of software used by planners and develop an innovative class of software fit for stewarding the cities of the coming century

Localism\u27s Ecology: Protecting and Restoring Habitat in the Suburban Nation

There is wide agreement among conservation activists and scientists alike that loss and alteration of habitat are the leading threats to biodiversity in America. Suburbs and exurbs, though, are only beginning to acknowledge that they are the problem in the struggle to stem the tide of “sprawl” and other economic processes producing ecosystem-wide habitat degradation today. A recent resurgence in academic and activist attention to local governments in America is reconsidering them as viable solutions to this problem. But most of this dialogue is being based upon a mistaken conception of local governance. Much of the legal scholarship on local environmentalism has ignored the reality of our localism and its role in the creation of the ever-expanding built landscape in America. This paper argues that this lack of realism in the current debate about local environmental law renders it blind to the vices of local governments and some of their sham conservation measures, but also to their counterintuitive virtues and possibilities for real conservation progress. Local government’s deep connection to private property entrepreneurialism is what has made it so practically powerful in resisting so many state and federal environmental initiatives. But it may well be this dimension of our localism that renders it uniquely fit to the tasks of real habitat protection and restoration in the twenty-first century

Weird City: Sense of Place and Creative Resistance in Austin, Texas

An increasing number of North American cities are demonstrating vocal resistance to perceived homogenization and corporatization of the urban landscape. In Austin, Texas, a grassroots movement has emerged as a form of resistance to these cultural and economic changes. "Keep Austin Weird," a slogan that has evolved from grassroots cultural movement to rallying cry for local business, is now being appropriated by numerous cities experiencing similar growth patterns (i.e. Boulder, Louisville, Albuquerque, and Portland, Oregon). This particular research is investigated in light of recent studies of the "Creative Class." Austin has been dubbed a Creative City success story by scholar Richard Florida and others, but is experiencing many challenges and externalities typical of growth in so-called Creative Cities. Ultimately, this research explores the inherent interconnections between sense of place, urban governance, and popular resistance. It also questions the potential sustainability of creative strategies for growth and the importance of civic participation. Keywords: Creative Cities, Sense of Place, Localization, Urban Landscape

Livability and Smart Growth

Lessons from a Surdna Foundation Initiativ