Although 70% of the Amazon population lives in urban areas, studies on the urban Amazon are scarce. Much of the urban Amazon population lives in precarious settlements. The distinctiveness and diversity of Amazonian precarious settlements are vast and must be identified to be considered in the development of appropriate public policies. Aiming at investigating precarious settlements in Amazon, this study is guided by the following questions: For the Brazilian Amazon region, is it possible to identify areas of precarious settlements by combining geoprocessing and remote sensing techniques? Are there different typologies of precarious settlements distinguishable by their spatial arrangements? Thus, we developed a methodology for identifying precarious settlements and subsequently classifying them into urban fabric typologies (UFT), choosing the cities of Altamira, Cametá, and Marabá as study sites. Our classification model utilized geographic objects-based image analysis (GEOBIA) and data mining of spectral data from WPM sensor images from the CBERS-4A satellite, jointly with texture metrics, context metrics, biophysical index, voluntary geographical information, and neighborhood relationships. With the C5.0 decision tree algorithm we carried out variable selection and classification of these geographic objects. Our estimated models show accuracy above 90% when applied to the study sites. Additionally, we described Amazonian UFT in six types to be identified. We concluded that Amazonian precarious settlements are morphologically diverse, with an urban fabric different from those commonly found in Brazilian metropolitan areas. Identifying and characterizing distinct precarious areas is vital for the planning and development of sustainable and effective public policies for the urban Amazon.

Identifying urban patterns in the cities in the Brazilian Amazon can help to understand the impact of human actions on the environment, to protect local cultures, and secure the cultural heritage of the region. The objective of this study is to produce a classification of intra-urban patterns in Amazonian cities. Concretely, we produce a set of Urban and Socio-Environmental Patterns (USEPs) in the cities of Santarém and Cametá in Pará, Brazilian Amazon. The contributions of this study are as follows: (1) we use a reproducible research framework based on remote sensing data and machine learning techniques; (2) we integrate spatial data from various sources into a cellular grid, separating the variables into environmental, urban morphological, and socioeconomic dimensions; (3) we generate variables specific to the Amazonian context; and (4) we validate these variables by means of a field visit to Cametá and comparison with patterns described in other works. Machine learning-based clustering is useful to identify seven urban patterns in Santarém and eight urban patterns in Cametá. The urban patterns are semantically explainable and are consistent with the existing scientific literature. The paper provides reproducible and open research that uses only open software and publicly available data sources, making the data product and code available for modification and further contributions to spatial data science analysis.

This paper describes {ActiveCA}, an open data product with Canadian time use data. {ActiveCA} is an R data package that contains analysis-ready data related to active travel spanning almost 40 years, extracted from Cycles 2 (1986), 7 (1992), 12 (1998), 19 (2005), 24 (2010), 29 (2015), and 34 (2022) of the Time Use Survey (TUS) from the General Social Survey (GSS). Active travel episodes are characterized by mode, with walking being part of every cycle and bicycling starting in 1992. The attributes of active trips are the types of locations of origins and destinations, the duration of trips, and episode weights for expanding the trips to population-wide estimates. Based on the year of the survey, a variety of locations are coded. In earlier cycles, these include home, work or school, and other’s home, whereas in later cycles these are augmented with locations such as grocery stores, restaurants, outdoor destinations, and others. The geographical resolution includes the province and whether the episode was in an urban or rural setting.

The increase in the spatial resolution of satellite imagery and the greater distribution of data have enabled the use of remote sensing for urban studies. However, there is still a lack of databases and cartographic publications for cities in the Brazilian Amazon. Thus, the following questions guided this work: what is the state-of-the-art in remote sensing to identify urban patterns in Brazil? Do these urban patterns equally cover all Brazilian regions? How to identify urban patterns in different contexts, such as the Amazon urban region? To this end, we conducted a review of publications that have identified urban patterns in Brazilian cities and put forward an identification model. For the most part, we observed that the works found analyzed small areas in São Paulo cities, using visual interpretation in private access and high spatial resolution imagery. To cover this gap, we propose a framework to identify Urban and Socio-Environmental Patterns based on remote sensing data, Voluntary Geographic Information data and census data. Although the framework can be applied in all regions of Brazil, the focus of the classification model is on Amazonian cities.

Transportation research has increasingly become interested in matters of justice given the extensive role it plays in society (Karner et al., 2020; Martens, 2016; Pereira et al., 2017; Pereira & Ka...