Expression of opinions are communicated on social media platforms, and these records can be analyzed and applied to a variety of spatial research questions. Using geo-tagged social media posts, specifically from the microblogging application Twitter, this research investigates the following research question: Assuming that users’ perceptions of crime can be accurately extracted and analyzed from social media data, to what degree do they match with actual geo-tagged crime incidents in time and space? This research could be serviceable to officers of the law for effective administration of citywide resources with the mission to weaken crime in areas most affecting residents’ perception of their safety. The City and County of San Francisco was chosen for its availability of crime data and Twitter data. Methods applied to explore the posited research questions included preprocessing the Twitter content to remove unnecessary information such as URLs plus retweets. Sentiment analysis in RStudio separated the entire corpus of tweets into emotional categories. Finally, a series of point density and emerging hotspots maps were created to explore the relationships within the data. Comparison between the hotspots maps for the rates of crime and for the rates of tweets reveal different similarities and discrepancies based upon the time range used for crime. Based on the comparison between spatial and temporal patterns of crime and tweets showing fear, the interpretation is that the northeastern areas in San Francisco were more likely to be the site of crime on Fridays around 6 p.m., plausibly larceny. Social media like Twitter may prove as effective indicators or perhaps even predictors of crime in space and time.
This dissertation examines the current state of automated indoor mapping and modeling using point cloud data produced by close range remote sensing systems. The first part looks at reality capture techniques that convert the physical form of indoor spaces into point clouds of millions of measured points, each with an (x,y,z) coordinate value. The second part examines methods for teasing out geometries from these point clouds -- often complicated by noise and voids -- and converting them into 3D geometric models. The final part examines techniques for merging the coordinate reference systems of these indoor maps and models with those of the outdoor world, resulting in a seamless representation of space. Lessons learned in this study revealed that theories, techniques, and practices in indoor mapping remain relatively elementary compared to those for the outdoors, yet they also present significant opportunities for future research propelled by emerging developments in remote sensing and a growing demand for indoor maps.
Crime is an important issue that any society has to deal with. Spatial analysis of crime is a valuable tool for tackling this problem, allowing patterns to be revealed, deeper understanding on the crime phenomenon to be attained and a more efficient allocation of policing and other public policies to be possible. Mapping and explaining spatial patterns of crime, however, involves many challenges. On this premise, this PhD dissertation has two main objectives: (1) presenting new methodologies to improve the spatial analysis of crime (2) applying these new methodologies to a case study — residential burglaries in Belo Horizonte — to evaluate what best explains the observed geography of crime. The new methodologies presented in this dissertation include a method for deciding on an adequate areal unit to count and map crime; a new approach for standardizing crime rates that provides more robust results than existing methods, and a model for estimating the effects of income and income inequality on the spatial distribution of crime within cities. Finally, some policy suggestions are discussed based on the results obtained by applying the methodology to the case of residential burglaries in Belo Horizonte.