Mariana Abrantes Giannotti

Flooding and inundation collaborative mapping – use of the Crowdmap/Ushahidi platform in the city of Sao Paulo, Brazil

The trend of using volunteered and collaborative data in the context of natural disasters has been increasing. This fact, together with floods and inundations, which occur in the city of Sao Paulo, makes it possible to explore the volunteered and collaborative way of generating and transmitting geographic data dynamically. This can be done by using technologies affordable to the population, such as the Internet, the global positioning system and other monitoring systems embedded in mobiles. This article aims to present the proposal of a conceptual scheme for a dynamic and collaborative mapping system of flooding points, whose data source comes from people equipped with mobile devices that allow identify their locations. The results correspond to the conceptual scheme of the system as well as the prototype ‘Flooding Points’ – a map available on the web showing the flooding points in the city, which were provided at the time of the event by ordinary people. The prototype was developed by using the free and open source Crowdmap/Ushahidi platform. The system was assessed by a questionnaire answered by the users, who gave their opinion about its feasibility, as well as the adjustments which must be made for the populations effective use. It was found that the application of system for subjects of inundation and flooding is complex in relation for other types of events due to its temporal dynamics characteristics. The results of the questionnaire, applied to evaluate the system, demonstrated the public utility of the application and the interest of the population for a dynamic system that enables the exchange of information on the problem of inundation and flooding in near real time in the city of Sao Paulo.

Characterization of Black Spot Zones for Vulnerable Road Users in São Paulo (Brazil) and Rome (Italy)

Non-motorized transportation modes, especially cycling and walking, offer numerous benefits, including improvements in the livability of cities, healthy physical activity, efficient urban transportation systems, less traffic congestion, less noise pollution, clean air, less impact on climate change and decreases in the incidence of diseases related to vehicular emissions. Considering the substantial number of short-distance trips, the time consumed in traffic jams, the higher costs for parking vehicles and restrictions in central business districts, many commuters have found that non-motorized modes of transportation serve as viable and economical transport alternatives. Thus, local governments should encourage and stimulate non-motorized modes of transportation. In return, governments must provide safe conditions for these forms of transportation, and motorized vehicle users must respect and coexist with pedestrians and cyclists, which are the most vulnerable users of the transportation system. Although current trends in sustainable transport aim to encourage and stimulate non-motorized modes of transportation that are socially more efficient than motorized transportation, few to no safety policies have been implemented regarding vulnerable road users (VRU), mainly in large urban centers. Due to the spatial nature of the data used in transport-related studies, geospatial technologies provide a powerful analytical method for studying VRU safety frameworks through the use of spatial analysis. In this article, spatial analysis is used to determine the locations of regions that are characterized by a concentration of traffic accidents (black zones) involving VRU (injuries and casualties) in Sao Paulo, Brazil (developing country), and Rome, Italy (developed country). The black zones are investigated to obtain spatial patterns that can cause multiple accidents. A method based on kernel density estimation (KDE) is used to compare the two cities and show economic, social, cultural, demographic and geographic differences and/or similarities and how these factors are linked to the locations of VRU traffic accidents. Multivariate regression analyses (ordinary least squares (OLS) models and spatial regression models) are performed to investigate spatial correlations, to understand the dynamics of VRU road accidents in Sao Paulo and Rome and to detect factors (variables) that contribute to the occurrences of these events, such as the presence of trip generator hubs (TGH), the number of generated urban trips and demographic data. The adopted methodology presents satisfactory results for identifying and delimiting black spots and establishing a link between VRU traffic accident rates and TGH (hospitals, universities and retail shopping centers) and demographic and transport-related data.

Mapeamento dinâmico e colaborativo de alagamentos na cidade de São Paulo

A tendencia de utilizacao de dados voluntarios e colaborativos em contextos de desastres naturais e crescente. Esse fato aliado aos cenarios de alagamentos que ocorrem na cidade de Sao Paulo traz a possibilidade de exploracao sobre o modo voluntario e colaborativo de geracao e transmissao da informacao geografica de forma dinâmica. E estas sao proporcionadas por tecnologias acessiveis a populacao, como o GPS (Global Positioning System) embarcado em celulares e a internet. O presente artigo tem como objetivo a proposta de um esquema conceitual para um sistema dinâmico e colaborativo de mapeamento dos pontos alagados, cuja fonte dos dados advem das pessoas equipadas com aparelhos celulares que permitem a sua localizacao. Os resultados apresentados correspondem aos esquemas conceituais do sistema, bem como ao prototipo “Pontos de Alagamento” - mapa disponibilizado via web com os pontos de alagamento da cidade, fornecidos no momento da ocorrencia do evento por pessoas comuns. O prototipo foi desenvolvido por meio da plataforma livre e de codigo aberto – Crowdmap/Ushahidi. O sistema foi avaliado atraves de um questionario respondido por usuarios, os quais opinaram sobre a viabilidade do mesmo, bem como os ajustes que devem ser realizados para o uso efetivo da populacao.

Active learning classification and change detection on multispectral images

The performance of image classification usually depends on the quality of labelled datasets to be used as training samples. In the context of remote sensing, the acquisition of ground-truth data can be a difficult and expensive task because it depends on the comprehensive surveys over the area of interest while the labelling task must be performed by experienced professionals. On the other hand, algorithms based on Active Learning can be helpful to overcome the lack of training samples. We present a cohesive algorithm for image classification and change detection based on Active Learning, that tackles the lack of ground-truth data. Afterwards, we compute the Principal Component Analysis over post-classification images to detect deforestation on the eastern side of So Paulo urban area. Our approach provides a way to automatically select data samples, while it also suggests a category. The user provides the category data (labelling task) to the selected pixels which are further used as training data in the final classification step. We applied the algorithm over four 6-channels multispectral images of the Landsat 5/TM device and we classified the pixels in two categories (“forest” and “non-forest”) for the years of 1986, 1996, 2003, and 2011. The change detection, is computed through an automatic threshold applied on the post-classification images. We were able to quantify de deforestation suffered by the eastern side of Sao Paulo city along the years. Our results show that the remaining 31% of forest in 1986 reach a minimum of 25% in 2003, but afterwards it recovered to 27% of the area in 2011.

Analyzing Urban Mobility Carbon Footprint with Large-scale, Agent-based Simulation.

The growth of cities around the world bring new challenges to urban management and planning. Tools, such as simulators, can help the decision-making process by enabling the understanding of the current situation of the city and comparison of multiple scenarios with regard to changes in the urban infrastructure and in public policy. This paper presents an analysis of mobility parameters, such as distance, cost, travel time, and carbon footprint, for different simulated scenarios in a large metropolis in a developing country. We simulated the scenarios using an open source, large-scale, agent-based Smart City simulator that we developed.

Estimates of forest degradation: An algorithm based on active learning, maximum likelihood and PCA for change detection

Land use management and control of deforestation in mega cities became an important problem due to pressure of urban sprawl. In addition, green areas and fountain-heads must be preserved in these cities, for the sake of living quality. For the purpose of monitoring deforestation and non-legal use of lands, this paper presents an approach to detect changes on the remaining areas of Atlantic Forest in São Paulo. We describe an algorithm for image classification and change detection based on the active learning, which is improved by a pre-clustering task, then a change detection step based on histogram analysis of the second PCA (Principal Component Analysis) component. We applied the algorithm over 4 multispectral Landsat 5/TM images representing the years 1986, 1996, 2003, 2011 of an eastern area of São Paulo city. With the proposed active learning technique we were able to categorize the pixels used in the change detection step. We estimated that the remaining 31% of forest in 1986 reaches a minimum of 25% in 2003, but afterwards it recovered to 27% of the total area in 2011.

Chracterizing urban land use patterns by variograms parameters from multispectral high spatial resolution satellite images: An application in Salvador, Bahia - Brazil

This article attempts to characterize urban land use patterns by variograms parameters from multispectral high spatial resolution satellite images. The variography is used to characterize variability and to characterize a land use urban pattern. Dataset compiled from Salvador, Bahia, Brazil, consists in single QuickBird satellite scene, geometrically corrected, obtained on August 2nd, 2005. Four land urban patterns were identified at the study area and characterized using remote sensing image classification parameters. The principal components were calculated over the variance-covariance matrix of the four spectral channels of the images and while the variograms were calculated on the first principal components of each of the four urban patterns. In general the use of the variogram to aid the classification has proved satisfactory for the study area. Parameters (sill, range and nugget effect) were valuable tools for classification areas and to characterize occupation patterns.

MAPAS NA WEB

This work deals with the recent trends of maps on the Web. It is based on the finding that the maps have been shaped to the characteristics of network communications, that requires the integration and sharing of information. The combination of these two goals, in computational vocabulary, is called interoperability. This is based on two distinct processes: standardization and systematization. On the Web, the volume of maps produced increased exponentially and its functionalities amplified. However, part of the terms related to maps become ephemeral due to constant innovations arising from technological development. One of the main challenges is to make the content of maps universally comprehensible by humans and machines. The Spatial Data Infrastructures, collaborative maps, wide range of localization strategies, digital earth and cloud computing reveal important trends of development.