Analysis of geospatial data for Urban Informatics applications: the case of Google Place in the city of Milan

EasyChair Preprint 941

Abstract

The paper is developed in five chapters divided into three parts: a first part of the state of the art, a second part concerning the description of the project and finally the last part of discussion of the results, conclusions and future developments.

The first chapter deals with the state of the art of Urban Informatics, new methods of analysis of urban dynamics and new spatial information. While the second chapter deals in detail with the state of the art of clustering methods and more specifically with spatial and hierarchical clustering on which the approach developed in this work is based.

While with chapters three and four they describe in detail the two main phases that make up the geospatial analysis approach. Chapter three, after an introduction to the features of the data offered by Google Place, continues by describing in detail the acquisition of data through an adaptive data collection method. While the fourth chapter initially describes the hierarchical-iterative clustering algorithm based on DBSCAN and subsequently deals with a preliminary analysis of the data.

Finally, the paper concludes with chapters five and six which respectively carry out the analysis of the classifications obtained by the clustering process and the evaluation of the meaning of these results. Specifically, the fifth chapter carries out an exploratory analysis on the data obtained, through a combination of methods of analysis and visualization of vector and geospatial data. While the sixth chapter is composed of the conclusions and possible future developments that emerged during the realization of this work.

Keyphrases: data clustering, data crawling, geospatial, urban analysis

@booklet{EasyChair:941,
  author    = {Domenico Monaco},
  title     = {Analysis of geospatial data for Urban Informatics applications: the case of Google Place in the city of Milan},
  howpublished = {EasyChair Preprint 941},
  year      = {EasyChair, 2019}}