Smart Cities

A program that works with public data bases from a city, using machine learning that obtains the urban and environmental behavior.

Smart Cities is a system that combines statistics and programming in order to provide an efficient, intelligent, and sustainable focus to urban planning. It works through storing databases produced by a city’s behavior, and then processing these through descriptive and inferential statistics and machine learning. The databases may be: daily pollution measures, population censuses, vehicular and pedestrian traffic flows, infrastructure and urbanism, etcetera. 

Smart Cities will generate maps that show which parts of a city have the best/worst air pollution measures and changes in it, as well as the status of pedestrian and vehicle traffic, and which areas of a city are more prone to natural disasters, such as floods, which is a common issue in Mexico. Then, based on this, the program will generate a second map with forecasts and suggestions as to which areas need attention from authorities regarding urban and environmental planning, as well as recommendations about land use.

The main issue concerning cities in Mexico, relies on the fact that there isn't adequate urban planning to allow vehicle traffic to decrease. Furthermore, industrial implementation is disproportionate, yet, governors and public officials can’t be blamed for this considering there is an absence of tools designed to provide the necessary knowledge regarding city behavior.

The matter in question exists all over Mexico, chances are it will likely exist within other developing cities in countries around the world. More often than not, it is only thought about implementing residential, industrial or commercial areas, without strategically planning the impact it could have on the environment. This problem takes a toll on all citizens, considering that the disproportionate growth of a city isn't going to solve its urban problems, but will rather intensify or create more of them, such as flooding due to sewer systems which cannot sustain the city’s poor urban design. When urban development isn’t prioritized as the city expands, it will progressively become harder to make it a better place. 

Some of the factors affecting good air quality fall back on an inadequate application of infrastructure, owing to the fact that an unfavorable urban quality such as sewage, transit, lighting, among other elements will lead a city to jam. This will happen as a result of having more demand than services available, given that there isn’t enough supply to sustain new citizens, hence the city, urban level wise, will wear away at a faster rate.

The solution serves whomever inhabits the urban areas in which the system can be installed. It is important to distinguish, however, the level at which it impacts different stakeholders. First of all, governments will benefit, since they will be aided in city planning as well as infrastructural development. This, on its own, will make the general population subject to an upgrade in quality of life, ranging from health issues to public safety, and better development conditions. Finally, as a cascade effect, health issues provoked by poor air quality will begin to be reduced, which will lower the impact of such affections in the public health systems.

Currently, the inhabitants of industrialized cities suffer from poor air quality and, in most cases, poorly planned urban growth. Smart Cities will help to stop these phenomena from happening, or reduce their impact. Air Quality will be able to be predicted and recommendations will be made regarding urban planning, this will guide the actions of governments and people to improve their quality of life.

We were first in contact with the problem of air quality thanks to a general studies course in our university, called “Citizenship and Intelligent Cities”. When we devised a manner to mitigate the effects of pollution, we noticed its uses could be expanded as we have presented in this document. We carried out a survey where we interviewed <100 persons about factors regarding air quality and the participation of different stakeholders in a scheme of governance for its solution.

The stage that the project currently resides is Concept considering that there has been a series of analyses where the main idea is refined through profound investigations. These researches include investigations from how an urban zone grows to the efficiency of applying new technologies such as machine learning. Understanding the behavior of cities and their citizens helps analyze and understand the main reasons for the spread of specific chemicals and how it affects air quality. Furthermore, it is essential to emphasize that apart from having a profound familiarity with the theory and data, the structure and functionality of the program are accounted for. The main focus would be to start with the coding to obtain predictions of how a city works concerning the poor quality of air. 

We ensured that our system contained a key technological factor, which is why it utilizes machine learning and data science. Should the reader not know what machine learning (ML) is, it can be understood as a branch of artificial intelligence that allows machines to understand and learn big chunks of information without the need of being explicitly programmed. ML is the pinnacle of pattern recognition, and it is capable of converting a data sample into a complex informatic program that extrapolates behavior and, therefore, has the ability to forecast future patterns. The program analyzes information and refines its predictions with each response it gets, given the fact that it is an artificial neural net.

Nowadays, several instances of internet development use ML on a daily basis. For instance, entertainment platforms, social networks, and intelligent assistants use it to enhance their understanding of each user and, as a consequence, enhance their user experience, designing a profile just for them.

All of these factors combined prove to be utterly important when it comes to designing and implementing modern solutions for modern issues. We must make any instance of human development benefit from these tools, which is why city growth can also be deeply impacted through direct, efficient and smart solutions for intelligent planning of residential, commercial, and industrial areas.

Our project has not yet been launched, given that it is still in a detailing phase. However, we estimate a potential to serve an average of 1,135,000 people per urban area, considering the size of the United Mexican States' main urban areas, and an average of 869,000 people in the country's rapidly growing cities.

• We want to help policymakers design public policies that will improve air quality in urban areas.
• We want to diminish diseases related with poor air quality.
• We want to serve as a tool in urban scaping, so that cities become more accesible and healthy for their inhabitants.
• We want to partner up with a city's government to test the efficacy of our solution, and show how our system has the potential to change the urban ecosystem in a positive way.

We are planning on making a Gannt chart where we establish a set of short and long–term goals, considering the UN's SDGs, and, primarily, those stated by Mexico's government to be the most crucial for the nation. We considered, namely, SDGs 3, 9, 11, and 13. Of these, we emphasized on SDGs 9.4 and 11.6, given their relevance to the Mexican reality.

So far, we have already presented the project to a municipal councilor in the Querétaro Municipality, capital of our home state (Querétaro), who showed great interest in our solution. We were, as a consequence, invited to a state-wide forum concerning sustainability to present it to higher-ranking policymakers.

We currently do not hold the financial stimuli needed to kickstart our project, which is a profound technical barrier, since we need to specialize on specific data science topics that require an investment. Concerning technical issues, there is an already existing air quality measurement system in most large urban areas. There may be, nonetheless, legal implications to use public infrastructure and information, which may involve a petition to the city council. Furthermore, there is a risk of contempt amongst taxpayers, albeit our surveys show a small likelihood for such events.

We are a multidisciplinary team with members from different areas of knowledge – two of our members are pursuing a college degree in engineering and the other three are persuing a degree in social sciences. The engineering students primarily focus on the numerical and technological parts of the solution, whilst the social sciences students provide the needed imputs regarding matters of governance and relations of the project whith the different stakeholders.

These factors provide the team with a multilateral and interdisciplinary overview on the issues being addressed, aiding in the pursual of finer and adjusted solutions.

We do not currently hold partnerships with any organizations.