DATA MINING TO EFFICIENT URBAN PLANNING HOW USE ARTIFICIAL INTELIGENCE AND BIG DATA IN SMART CITIES
MIRIAN MAIA ADVISOR TEACHERS : Sandra Nogueira and Agnaldo Reis
Justification The technological evolution created new demands. The future's urbanist needs work with processing data in new cities, the smart cities, that will use artificial intelligence, mining data and blockchain on day by day. The justify to this research is show exemples of use of big data and suggest the better use of data to create efficiency to master plans and urban solutions , to cities more real and flexible, with sustentability and transportation more useful to citizens on day by day.
Michael Batty in “How Can Big Data Be Used in Urban Planning?” Font: http://www.alexandrinepress.co.uk/ planning-with-big-data
“The issue of Built Environment on ‘Big Data and the City’ contains some revealing prospects of how these new streams of data that are largely coming from sensors embedded in the environment can help us understand the city better.. [...] This data is changing the way we look at the city – it is changing our focus from the medium and the long term to the very short term: to what happens in cities over second, minutes and hours, if not days and weeks rather than over years and decades that have traditionally formed the focus of urban analysis and planning.”
About the problem
Master Plans and other urban laws starts out because delay to get informations to get userful data (and have case that doesn't use any data to create these documents). Have a real problem of urban laws planning that don't follow the evolution and organicity and onstruction of urban fabric, and it create cousts unnecessary and make "solutions" that doesn't work. So, we suggest a type/ applicantion that mine these data using artificial inteligence that will agree with the new demands that will evolute with time, generating flexibility of urban planning, creating an approaching path of people with laws and making possible that enabling resources and public system to really be useful for citizens real needs.
In cities with more of 200 000 people:
20%
Of brazilian cities have some use of technology to improviment of use of city Font: Cadernos FGV Cidades Inteligentes e Mobilidade Urbana
"[...]tecnologia para gerenciamento de tráfego, o que inclui semáforos inteligentes, câmeras de vigilância, sinais eletrônicos e sistemas para gerenciar o transporte público. " When in big brazilian cities have some application that uses big data to urban planning, normally is used only to manager transportation and security, with less or none use of artificial intelligence and mining data to these systems. Porto Alegre, São Paulo and Rio de Janeiro are making some tests with big data processing to participatory budgeting and reduce use of water and energy.
Chinese Exemple In 2014, with the use of data collected by map applications, GPS of taxis and buses, hotspots in important places of the city and voluntary insertion of information, urban planners and data mining professionals were together to create an information network among the largest cities in China, making a data base about urban policies that could be more or less deepened according to the need of each city or micro-region. With this study, was observed the macro level of construction of the urban fabric, where to go, how to improve, the failures of the current model, how improve the flexibility of uses of space and better allocation of resources, opening of roads, construction of new train lines and metro and efficiency of public transport.
Font: The rise of big data on urban studies and planning practices in China: Review and open research issues . Journal of Urban Management
Goals
General Goals Optimize the quality of life of the residents with greater efficiency of public services, mobility and most access to urban laws.
Goals
Specific Goals 1) To investigate how to use the big data to create urban laws, specifically, more pragmatic and objective master plans, with possibility of flexibility, according to the development of new techniques and the approximation of the population with the state.
Goals
Specific Goals 2) Compare similar cities using / used urban solutions based on data mining with cities that don't use these solutions. 3)Conception of more objective solutions, such as the possible creation of a prototype of some application that uses artificial intelligence and data mining according to the proposals that was presented.
METHODOLOGY: Study of scientific articles About the use of urban technologies and general urbanism,internet of things, artificial intelligence, data mining and blockchain.
Case study Case study on the use of these technologies, based on master's and doctoral theses and other research on the use of AI, IoT and Data Minning
Documental analysis
Technology Proposal
Analysis of documents and urban studies of cities that use and don't use this technology, highlighting common aspects of what the use of data mining can contribute
Elaboration of a concept and, if possible, an application that can be functional and present suggestions for improvements.
INDEX
1. INTRODUCTION 1.1 Justification 1.2 Goals 1.2.1 General Goals 1.2.2 Specific Goals 1.3 methodology
2. BIG DATA, IOT AND AI 2.1 About processing and mining data 2.2 Exemples to day by day 2.2.1 Exemple 1 2.2.2 Exemple 2 2.3 Urban Solutions exemples 2.3.1 Exemple 1 2.3.2 Exemple 2
3. USE IN PRACTICE 3.1 Project proposal 3.2 Project rules 3.3 Obtained data 3.3.1 Data 1 3.3.2 Data 2 3.4 Case study using the proposal 3.4.1 Improviments that we could make 3.4.2 Difficulties for deployment
4. APPLICATON 4.1 How it works 4.2 Obtained data 4..2.1 Data 1 4.2.2 Data 2
4. FINAL CONSIDERATIONS 4.1 Conclusion 4.2 Bibliography
MAR/APR. 2019: START THE RESEARCH Reading and analysis of monographs, theses, articles and examples relevant to the research. Start learning concepts of mining data, big data, internet of things and blockchain.
ABR/MAY. 2019: START THE WRITTING Continuation of the research. Start of base text for bachelor thesis. Learning programming methods in Python and R.
SCHEDULE What we hope do
JUN/JUL. 2019: START OF DATA COLLECTION Data collection and examples for comparison of urban planning and urban laws. Comparison of cases. Creation of graphs, images and algorithms.
AUG/SEP. 2019: TEXT POLISH AND START OF DATA STANDARDIZATION Polishing text for final presentation and start of data standardization for data mining tests
TFG1
OCT/NOV. 2019: END OF TEXT AND TESTS Finalization of the text for presentation in December. Start of AI prototype and tests
TFG2 DEC. 2019: FINAL TESTS AND PRESENTATION Completion of prototype tests. Inclusion of results in the final text. Presentation for banking.
BIBLIOGRAPHY: 1) HAO, J.; ZHU, J.; ZHONG, R. The rise of big data on urban studies and planning practices in China: Review and open research issues. Journal of Urban Management, v. 4, n. 2, p. 92–124, 2015. 2) CADERNOS FGV PROJETOS. Cidades Inteligentes e Mobilidade Urbana. Ano 9, n. 24, julho 2014. 3) BORTOLUCI, J. H. Architectures Of Democracy: Housing Movements And Progressive Architects In São Paulo (1970-1990). Estudos Históricos (Rio de Janeiro), v. 31, n. 65, p. 369–388, 2018. 4) GLAESER, E. et al. Big Data and Big Cities: The Promises and Limitations of Improved Measures of Urban Life. 2015. 5) LUCK, M.; AYLETT, R. Applying artificial intelligence to virtual reality: Intelligent virtual environments. Applied Artificial Intelligence, v. 14, n. 1, p. 3–32, 2000. 6) SANTANDER, A. A.; GARAI-OLAUN, A. A.; ARANA, A. D. L. F. Historic Urban Landscapes: A Review on Trends and Methodologies in the Urban Context of the 21st Century. Sustainability, v. 10, n. 8, p. 2603, 2018. International Conference on Big Data and Smart City 2016. 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), 2016.
BIBLIOGRAPHY: 7)International Conference on Big Data and Smart City 2016. 2016 3rd MEC International Conference on Big Data and Smart City (ICBDSC), 2016. 8) KITCHIN, R. The Real-Time City? Big Data and Smart Urbanism. SSRN Electronic Journal, 2013. 9) MICHELIN, R. A. et al. SpeedyChain. Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services - MobiQuitous '18, 2018. 10) PIERONI, A. et al. Smarter City: Smart Energy Grid based on Blockchain Technology. International Journal on Advanced Science, Engineering and Information Technology, v. 8, n. 1, p. 298, 2018. 11) RATHORE, M. M. et al. Urban planning and building smart cities based on the Internet of Things using Big Data analytics. Computer Networks, v. 101, p. 63–80, 2016. 12) Tecnologias de Mobilidade Urbana no Brasil. DisponĂvel em: https://techinbrazil.com.br/tecnologias-inteligentes-de-mobilidade-urbana-no-brasil . Acesso 01/03/2019
BIBLIOGRAPHY: 13) How can Big Data be used in Urban Planning ?. Disponível em: http://www.alexandrinepress.co.uk/planning-with-big-data. Acesso 02/03/2019 14) O caso da primeira Smart City do Brasil. Disponível em: https://www.archdaily.com.br/br/888323/cidades-fabricadas-o-caso-da-primeira-smart-city-dobrasil Acesso em 02/03/2019 15) What NASA can teach to urban planners. Disponível em: https://www.citylab.com/design/2015/11/what-nasa-can-teach-urban-planners/415467/ .Acesso em 02/03/2019. 16) NASA - Landscape and Urban Planning . Disponível em: https://landsat.gsfc.nasa.gov/wpcontent/uploads/2013/02/2010_UrbanFS.pdf . Acesso em 02/03/2019 17) How Urban Planning Works. Disponível em: https://science.howstuffworks.com/environmental/green-science/urban-planning5.htm . Acesso em 02/03/2019