A LARGE SCALE CLASSIFICATION OF PUBLIC SPACES USING A STUDY OF PERCEPTION AND PUBLIC DATA FROM INEGI OF THE CITY OF PUEBLA, SAN PEDRO CHOLULA AND SAN ANDRES CHOLULA
Keywords: Perception, Machine Learning, Classification, City Planning, Public Places, Multi Layer Perceptron, Crowdsourcing
Abstract. This work aims to create a methodology to automatize the classification of public spaces using a perception test and data obtained from city census information, in our case, from the Mexican National Institute of Statistics and Geography (INEGI). Nowadays there is no well defined process in decision making when planning the creation or development of public spaces. For this reason, a study to measure the human perception was made in order to gather data about what people perceived about five variables: architectural beauty, pollution, fun, wealth and safety. The information obtained was used to create a Machine learning model that could find a relation between the perception obtained and the census dataset. This first attempt aims to find key insights needed to develop a more complex methodology to classify, at a greater scale, public places in terms of their safety or architectural value and which socio-demographic data defines this perception.