Mammalian carnivores are considered a key group in maintaining ecological health and can indicate potential ecological integrity in landscapes where they occur. Carnivores also hold high conservation value and their habitat requirements can guide management and conservation plans. The order Carnivora has 84 species from 8 families in the Neotropical region: Canidae; Felidae; Mephitidae; Mustelidae; Otariidae; Phocidae; Procyonidae; and Ursidae. Herein, we include published and unpublished data on native terrestrial Neotropical carnivores (Canidae; Felidae; Mephitidae; Mustelidae; Procyonidae; and Ursidae). NEOTROPICAL CARNIVORES is a publicly available data set that includes 99,605 data entries from 35,511 unique georeferenced coordinates. Detection/non‐detection and quantitative data were obtained from 1818 to 2018 by researchers, governmental agencies, non‐governmental organizations, and private consultants. Data were collected using several methods including camera trapping, museum collections, roadkill, line transect, and opportunistic records. Literature (peer‐reviewed and grey literature) from Portuguese, Spanish and English were incorporated in this compilation. Most of the data set consists of detection data entries (n = 79,343; 79.7%) but also includes non‐detection data (n = 20,262; 20.3%). Of those, 43.3% also include count data (n = 43,151). The information available in NEOTROPICAL CARNIVORES will contribute to macroecological, ecological, and conservation questions in multiple spatio‐temporal perspectives. As carnivores play key roles in trophic interactions, a better understanding of their distribution and habitat requirements are essential to establish conservation management plans and safeguard the future ecological health of Neotropical ecosystems. Our data paper, combined with other large‐scale data sets, has great potential to clarify species distribution and related ecological processes within the Neotropics. There are no copyright restrictions and no restriction for using data from this data paper, as long as the data paper is cited as the source of the information used. We also request that users inform us of how they intend to use the data.

The Deepwater Horizon blowout in the Gulf of Mexico resulted in one of the largest accidental oil disasters in U.S. history. NASA acquired radar and hyperspectral imagery and made them available to the scientific community for analyzing impacts of the oil spill. In this study, we use the L-band quad-polarized radar data acquired by Unmanned Aerial Vehicle Synthetic Aperture Radar (UAVSAR) and Hyperspectral Imagery (HSI) from the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) optical sensor. The main objective of this research is to apply fusion techniques on polarimetric radar and hyperspectral imagery to investigate the benefit of fusion for improved classification of coastal vegetation contaminated by oil. In this approach, fusion is implemented at the pixel level by concatenating the hyperspectral data with the high resolution SAR data and analyze the fused data with Support Vector Machine (SVM) classification algorithm.

The Amazon forest has the highest biodiversity on Earth. However, information on Amazonian vertebrate diversity is still deficient and scattered across the published, peer‐reviewed, and gray literature and in unpublished raw data. Camera traps are an effective non‐invasive method of surveying vertebrates, applicable to different scales of time and space. In this study, we organized and standardized camera trap records from different Amazon regions to compile the most extensive data set of inventories of mammal, bird, and reptile species ever assembled for the area. The complete data set comprises 154,123 records of 317 species (185 birds, 119 mammals, and 13 reptiles) gathered from surveys from the Amazonian portion of eight countries (Brazil, Bolivia, Colombia, Ecuador, French Guiana, Peru, Suriname, and Venezuela). The most frequently recorded species per taxa were: mammals: Cuniculus paca (11,907 records); birds: Pauxi tuberosa (3713 records); and reptiles: Tupinambis teguixin (716 records). The information detailed in this data paper opens up opportunities for new ecological studies at different spatial and temporal scales, allowing for a more accurate evaluation of the effects of habitat loss, fragmentation, climate change, and other human‐mediated defaunation processes in one of the most important and threatened tropical environments in the world. The data set is not copyright restricted; please cite this data paper when using its data in publications and we also request that researchers and educators inform us of how they are using these data.

The location of logistics facilities is important for urban freight transport. Several metropolitan regions have identified that logistics facilities are relocating to suburban areas outside city center boundaries. This phenomenon is known as logistics sprawl. In this paper, we present an analysis of the logistics sprawl phenomenon in the Belo Horizonte Metropolitan Area (Brazil) between 1995 and 2015, through spatial analysis. The results show a logistics sprawl indicator (average change in the standard distance to the center of gravity) of 1.2 km (17.8 km in 1995 and 19 km in 2015). In addition, we explored the spatial correlation between socioeconomic data and the location of warehouses. In 2015, 80% of warehouses were located in the high-income area. Additionally, most of the warehouses were located within a two-kilometer buffer from the axis of the road and in a five-kilometer buffer from the railroad. Finally, we defined the service areas of the warehouses considering a maximum distance through the network of 5, 10, and 15 km. In 2015, the 15-km service area had a coverage of 89% of the population in the study area.

Policy and legislation rarely acknowledge the importance of keeping intact ecosystems road- and railroad-free. By modeling Brazil’s remaining roadless and railroad-less (RLRL) areas, we found that, although they hold the vast majority of the country’s remaining native vegetation (81.5%), because of their limited protection status, only 38% of Brazil’s remaining native vegetation is both protected and in RLRL areas. Current federal policy aims to develop transportation infrastructure designed with antiquated planning methods that threaten remaining intact ecosystems, while concurrently weakening the country’s hallmark environmental protections and commitments. Where Brazil builds its new roads and railroads matters for conservation planning. The occurrence of native vegetation and anthropic land use is associated, at varying degrees, to transportation infrastructure throughout most of Brazil. We highlight that by pursuing conservation opportunities in RLRL areas, Brazil could instead make impactful steps for conservation, restoration planning, and tangible progress toward achieving national and international environmental and conservation commitments.

Today, one of the main challenges faced in urban logistics is the distribution of goods. In Brazil, mid to large cities have experienced consequences of unplanned urban sprawl and lack of adequate transportation infrastructure. The relationship between urban planning and transport stands out the attractiveness of some urban activities with direct impacts on the movement of people and goods and other component elements of urban space. The segment of bars and restaurants falls within this context, therefore is a vital activity responsible for significant percentage of jobs and revenue in a city. Altogether, foods & beverages commercial activities move daily large volumes of goods to meet the need of customers. This paper presents the results of a freight trip generation model developed for pubs and restaurants in Belo Horizonte (Brazil). Once performed the model determined the number of trips generated per day per establishment. In order to expand the discrete result to a continuous one, the results were geographically interpolated to a continuous surface and extrapolated within the city limits. The data for the freight trip generation model were obtained by survey. For this, we designed a structured questionnaire to obtain information about goods, frequency, operational time, place of performance of the loading/unloading of goods, establishment size and the number of employees. Besides these information, we investigated the acceptance of alternative practices in the delivery of goods, such as off-peak delivery. To accomplish the proposed models, we applied a simple linear regression, correlating the following variables: (i) Number of trips versus area of the establishment; (ii) Number of trips versus number of employees; (iii) Number of trips versus operation day of the establishment. With the results of the linear regression for travel generations, conducted the data interpolation based on the standard deviation of the results to define the sample classification bands. This interpolation method was chosen because it is one of the most suitable for analysis of spatially scattered points due to the straightforwardness of the model and because it does not consider extra noise such as slope and spatial constraints as barriers. In this method, interpolation is determined by the value assigned to each point (in this case the number of trips), wherein the closer the points the higher the correlation trend. Finally, the resulting trip generation surface was analysed together with other geographic data such as demographic data, road network density and socioeconomic data. Findings indicate the importance of a mathematic-geographic model for trip generation as a feasible approach for support transportation planning & operation for urban goods distribution. Critical information such as the high concentration of pubs and restaurants in the same region can reinforce the vocation of the city for trading. However, an elevated number of freight vehicles to meet a high and growing demand becomes a problem specially in areas where urban road network is not efficient (not properly designed and parking spaces not properly used). This study also highlights the need for an urban freight mobility plan and public policies, by offering sustainable alternatives for urban goods distribution, which improve the urban environment. By using geospatial analysis, the study delivered statistics data and maps to catch the attention of decision makers and transportation managers, therefore facilitate the discussion on transportation policies in the city of Belo Horizonte.