Dataset Overview

This dataset provides a comprehensive overview of Brazil’s 2022 Census data, focusing on São Paulo’s neighbourhoods. The data combines demographic and socioeconomic information with geospatial shapefiles of São Paulo’s neighbourhoods, enabling users to perform statistical and spatial analyses.

Users can explore patterns, trends, and transformations in São Paulo’s urban landscape by linking census sectors to neighbourhood boundaries.

Key Components

Census 2022 Data

• Source: Brazil's 2022 Census (IBGE)
• Content: Demographic data including age, gender, income, education levels, household size, and population density across census sectors.
• Format: CSV

São Paulo Neighborhood Shapefile

• Source: GIS-based shapefiles for São Paulo neighbourhoods (IBGE Census Sectors and Manually created Neighbourhoods)
• Content: Spatial geometry for São Paulo's neighbourhoods with census sector identifiers.
• Format: Parquet

Use Cases

• Neighborhood Demographics Analysis: Combine census data with shapefiles to generate neighborhood-level demographic reports.
• Urban Development Studies: Study how São Paulo neighbourhoods have grown using historical context and 2022 Census data.
• Spatial Data Visualizations: Create maps showing income distribution, population density, or other demographic factors across neighbourhoods.
• Policy Planning & Research: Support urban planning, resource allocation, and policy development in São Paulo.

Potential Applications

• Analyze the relationship between neighbourhood demographics and urban growth patterns.
• Visualize inequalities in population distribution, income, or education levels.
• Identify trends in housing and population density for urban studies.
• Provide insights into São Paulo’s historical and ongoing transformations.

Why Use This Dataset?

• Comprehensive Coverage: Detailed census data and spatial boundaries allow in-depth analyses.
• Flexible Integration: Easily combine demographic data with shapefiles to enable advanced spatial analyses.

Dataset Details

• File Formats: CSV (Census Data), GeoJSON/Shapefile (Neighborhood Shapefiles)
• Spatial Resolution: Census sector linked to São Paulo’s neighbourhood boundaries

Geographic Scope: São Paulo, Brazil

This dataset is ideal for data scientists, urban planners, and researchers seeking to uncover the dynamics of São Paulo’s neighbourhoods through an intersection of demographic and spatial data.

Contribute to new insights and empower decision-making in understanding Brazil’s largest city!

The Global Human Footprint dataset of the Last of the Wild Project, version 2, 2005 (LWPv2) is the Human Influence Index (HII) normalized by biome and realm. The HII is a global dataset of 1 km grid cells, created from nine global data layers covering human population pressure (population density), human land use and infraestructure (built-up areas, nighttime lights, land use/land cover) and human access (coastlines, roads, navigable rivers).The Human Footprint Index (HF) map, expresses as a percentage the relative human influence in each terrestrial biome. HF values from 0 to 100. A value of zero represents the least influence -the "most wild" part of the biome with value of 100 representing the most influence (least wild) part of the biome.

In 2023, the highest population number by districts in São Paulo was for the Grajaú municipality with approximately ******* inhabitants, followed by Jardim Ângela with around ******* and Capão Redondo with *******.

The present dataset is part of the published scientific paper entitled “The role of spatial planning in land change: An assessment of urban planning and nature conservation efficiency at the southeastern coast of Brazil” (Pierri Daunt, Inostroza and Hersperger, 2021). In this work, we evaluated the conformance of stated spatial planning goals and the outcomes in terms of urban compactness, basic services and housing provision, and nature conservation for different land-use strategies. We evaluate the 2005 Ecological-Economic Zoning (EEZ) and two municipal master plans from 2006 in a coastal region in São Paulo State, Brazil. We used Partial Least Squares Path Modelling (PLS-PM) to explain the relationship between the plan strategies and land-use change ten years after implementation in terms of urban compactness, basic services and housing increase, and nature conservation. We acquired the data for the explanatory variables from different sources listed on Table 1. Since the model is spatially explicit, all input data were transformed to a 30 m resolution raster. Regarding the evaluated spatial plans, we acquired the zones limits from the São Paulo State Environmental Planning Division (CPLA-SP), Ilhabela and Ubatuba municipality. 1) Land use and cover data: Urban persistence, Urban axial, Urban infill, Urban Isolates, Forest cover persistence, Forest cover gain, NDVI increase We acquired two Landsat Collection 1 Higher-Level Surface Reflectance images distributed by the U.S. Geological Survey (USGS), covering the entire study area (paths 76 and 77, row 220, WRS-2 reference system, https://earthexplorer.usgs.gov/). We classified one image acquired by the Landsat 5 Thematic Mapper (TM) sensor on 2005-05-150, and one image from the Landsat 8 Operational Land Imager (OLI) sensor from 2015-08-15. We collected 100 samples for forest cover, 100 samples for built-up cover and 100 samples for other classes. We then classified these three classes of land cover at each image date using the Support Vector Machine (SVM) supervised algorithm (Hsu et al., 2003), using ENVI 5.0 software. Land-use and land-cover changes from 2005 to 2015 were quantified using map algebra, by mathematically adding them together in pairs (10*LULC2015 + LULC2005). We reclassified the LULC data into forest gain (conversion of any 2005 LULC to forest cover in 2015); forest persistence (2005 forested pixels that remained forested in 2015); new built-up area (conversion of any 2005 LULC to built-up in 2015); and urban maintenance (2005 built-up pixels that remained built-up in 2015). To describe the spatial configuration of the urban expansion, we classified the new built-up areas into axial, infill and isolated, following Inostroza et al. (2013) (For details, please refer to Supplementary Material I at the original publication). The NDVI was obtained from the same source used for the LULC data. With the Google Engine platform, we used an annual average for the best pixels (without clouds) for 2005 and 2015, and we calculated the changes between dates. We used increases of - 0.2 NDVI to represent an improvement in forest quality. 2) Federal Census data organization: Urban Basic Services and Housing indicator, socioeconomic and population: The data used to infer the values of basic services provision, socioeconomic and population drivers was derived from the Brazilian National Census data (IBGE, 2000 and 2010). Population density, permanent housing unit density, mean income, basic education, and the percentage of houses receiving waste collection, sanitation and water provision services, called basic services in the context of this study, were calculated per 30 m pixel. The Human Development Index is only available at the municipality level. We attributed the HDI for the vector file with the municipality border, and we rasterized (30 m resolution) this file in QGIS. Annual rates of change were then calculated to allow comparability between LULC periods. To infer the BSH, we used only areas with an increase in permanent housing density and basic services provision (See Supplementary Material I at the original publication). 3) Topographic drivers To infer the values of the topographic driver, we used the slope data and the Topographic Index Position (TPI) based on the digital elevation model from SRTM (30 m resolution) produced by ALOS (freely available at eorc.jaxa.jp/ALOS/en/about/about_index.htm), and both variables were considered constant from 2005 to 2015 (See Supplementary Material I at the original publication).

Abstract At the end of 2019, the outbreak of COVID-19 was reported in Wuhan, China. The outbreak spread quickly to several countries, becoming a public health emergency of international interest. Without a vaccine or antiviral drugs, control measures are necessary to understand the evolution of cases. Here, we report through spatial analysis the spatial pattern of the COVID-19 outbreak. The study site was the State of São Paulo, Brazil, where the first case of the disease was confirmed. We applied the Kernel Density to generate surfaces that indicate where there is higher density of cases and, consequently, greater risk of confirming new cases. The spatial pattern of COVID-19 pandemic could be observed in São Paulo State, in which its metropolitan region standed out with the greatest cases, being classified as a hotspot. In addition, the main highways and airports that connect the capital to the cities with the highest population density were classified as medium density areas by the Kernel Density method.It indicates a gradual expansion from the capital to the interior. Therefore, spatial analyses are fundamental to understand the spread of the virus and its association with other spatial data can be essential to guide control measures.

Although Aedes fluviatilis is an anthropophilic mosquito found abundantly in urban environments, its biology, epidemiological potential and genetic characteristics are poorly understood. Climate change and urbanization processes that result in environmental modifications benefit certain anthropophilic mosquito species such as Ae. fluviatilis, greatly increasing their abundance in urban areas. To gain a better understanding of whether urbanization processes modulate the genetic structure of this species in the city of São Paulo, we used eight microsatellite loci to genetically characterize Ae. fluviatilis populations collected in nine urban parks in the city of São Paulo. Our results show that there is high gene flow among the populations of this species, heterozygosity deficiency and low genetic structure and that the species may have undergone a recent population expansion. There are two main hypotheses to explain these findings: (i) Ae. fluviatilis populations have undergone a population expansion as a result of urbanization; and (ii) as urbanization of the city of São Paulo occurred recently and was quite intense, the structuring of these populations cannot be observed yet, apart from in the populations of Ibirapuera and Piqueri parks, where the first signs of structuring have appeared. We believe that the expansion found in Ae. fluviatilis populations is probably correlated with the unplanned urbanization of the city of São Paulo, which transformed green areas into urbanized areas, as well as the increasing population density in the city.

SARS-CoV-2 spread rapidly in the Brazilian Amazon. Mortality was elevated, despite the young population, with the health services and cemeteries overwhelmed. The attack rate in this region is an estimate of the final epidemic size in an unmitigated epidemic. Here we show that by June, one month after the epidemic peak in Manaus, capital of the Amazonas state, 44% of the population had detectable IgG antibodies. This equates to a cumulative incidence of 52% after correcting for the false-negative rate of the test. Further correcting for the effect of antibody waning we estimate that the final attack rate was 66%. This is higher than seen in other settings, but lower than the predicted final size for an unmitigated epidemic in a homogeneously mixed population. This discrepancy may be accounted for by population structure as well as some limited physical distancing and non-pharmaceutical measures adopted in the city.

Methods Selection of blood samples for serology testing

Both the FPS and HEMOAM blood centers routinely store residual blood samples for six months after donation. In order to cover a period starting from the introduction of SARSCoV-2 in both cities, we retrieved stored samples covering the months of February to May in São Paulo, and February to June in Manaus, at which point testing capacity became available. In subsequent months blood samples were prospectively selected for testing. The monthly target was to test 1,000 samples at each study site. However, due to problems with purchasing the kits, supply chain issues, and the period of test validity, some months were under and others over the target (to avoid wasting kits soon to expire). We aimed to include donations starting from the second week of each month. Part of the remit of the wider project is to develop a system to prospectively select blood donation samples, based on the donor’s residential address, so as to capture a spatially representative sample of each participating city. For example, FPS receives blood donations from people living across the whole greater metropolitan region of São Paulo. The spatial distribution of donors does not follow the population density, with some areas over- and others under-represented. We used residential zip codes (recorded routinely at FPS) to select only individuals living within the city of São Paulo. We then further divided the city into 32 regions (subprefeituras) and used their projected population sizes for 2020 to define sampling weights, such that the number of donors selected in any given subprefeitura was proportional to the population size. We piloted this approach in São Paulo and have developed an information system to operationalize this process at the participating center. However, at the time of data collection the system was not implemented in HEMOAM and therefore it was not possible to use this sampling strategy. As such, we simply tested consecutive blood donations, beginning from the second week of each month until the target was reached.

Quantifying antibody waning and rate of seroreversion

We sought to quantify the rate of decline of the anti-nucleocapsid IgG antibody that is detected by the Abbott CMIA. We tested paired serum samples from our cohort of convalescent plasma donors (described above). We calculated the rate of signal decay as the difference in log2 S/C between the first and second time points divided by the number of days between the two visits. We used simple linear regression to determine the mean slope and 95% CI.

Analysis of seroprevalence data

Using the manufacturer's threshold of 1.4 S/C to define a positive result we first calculated the monthly crude prevalence of anti-SARS-CoV-2 antibodies as the number of positive samples/total samples tested. The 95% confidence intervals (CI) were calculated by the exact binomial method. We then re-weighted the estimates for age and sex to account for the different demographic make-up of blood donors compared to the underlying populations of São Paulo and Manaus (Fig. S4). Because only people aged between 16 and 70 years are eligible to donate blood, the re-weighting was based on the projected populations in the two cities in this age range only. The population projections for 2020 are available from (https://demografiaufrn.net/laboratorios/lepp/). We further adjusted these estimates for the sensitivity and specificity of the assay using the Rogan and Gladen method As a sensitivity analysis, we took two approaches to account for the effect of seroreversion through time. Firstly, the manufacturer's threshold of 1.4 optimizes specificity but misses many true-cases in which the S/C level is in the range of 0.4 – 1.4 (see ref and main text). In addition, individuals with waning antibody levels would be expected to fall initially into this range. Therefore, we present the results using an alternative threshold of 0.4 to define a positive result and adjust for the resultant loss in specificity. Secondly, we corrected the prevalence with a model-based method assuming that the probability of seroreversion for a given patient decays exponentially with time. In the model-based method for correcting the prevalence, only the months between March and August were considered. The measured prevalence used as input for this method was obtained using the manufacturer’s threshold of 1.4, and the correction based on the test specificity (99.9%) and sensitivity (84%) was applied, as well as the normalization by age and sex. Confidence intervals were calculated through bootstrapping, assuming a beta distribution for the input measured prevalence. It is worth noting that even though this model is limited by the exponential decay assumption, assuming distributions with more degrees of freedom may lead to overfitting due to the small number of samples of 9[7]. Finally, the obtained values for - and " must be interpreted as parameters for this model, and not estimates for the actual decay rate and seroreversion probability as they may absorb the effect of variables that are not taken into account by this model.

Infection fatality ratio

We calculated the global infection fatality ratio in Manaus and São Paulo. The total number of infections was estimated as the product of the population size in each city and the antibody prevalence in June (re-weighted and adjusted for sensitivity and specificity). The number of deaths were taken from the SIVEP-Gripe system, and we used both confirmed COVID-19 deaths, and deaths due to severe acute respiratory syndrome of unknown cause. The latter category likely represents COVID-19 cases in which access to diagnostic testing was limited , and more closely approximate the excess mortality. We calculated age-specific infection fatality ratios by assuming equal prevalence across all age groups.

Effective reproduction number

We calculated the effective reproduction number for São Paulo and Manaus using the renewal method9, with the serial interval as estimated by Ferguson (2020)10. Calculations were made using daily severe acute respiratory syndrome cases with PCR-confirmed COVID-19 in the SIVEP-Gripe system. Region-specific delays between the PCR result release and the date of symptom onset were accounted for using the technique proposed by Lawless (1994).

The Southeast Region in Brazil, was the region with the highest density of surgeons in the country in 2022, with **** surgeons per 100,000 people. The most populated cities in Brazil, like Rio de Janeiro and São Paulo, are located in this region. That year, São Paulo was the city with the highest number of doctors in the country.

Abstract In light of the forms of access to housing for low-income population in Brazil, this paper analyzes, specifically, the living conditions of slum dwellers using available census data. It evaluates the meaning of living in a slum in Brazil during the first decade of the 21st century according to some key issues: was there an increase in the number of slum dwellers in Brazil? Where was this increase most significant? Was this increase produced by new slums or by the increase in slums’ population density? What are the characteristics of slum households? Was there any improvement in infrastructure-related indicators? The paper is also an unprecedented effort to analyze the 2010 Census "Territorial Reading", a single database for slum households.

Context

One criteria that can be used to measure the water quality of the beaches is the density of Enterococcus, which indicates the fecal pollution in water. High values of the density of this bacteria indicates that the water quality is compromised and it represents risks to the bathers health. The measurements are performed weekly in several beaches in the state of São Paulo and they are used to classify the beaches as 'Proper' or 'Improper' for bathing.

Content

This data represents over 68k measurements of Enterococcus taken weekly between 2012 and 2021, from 16 cities and 168 beaches/collection points in the São Paulo state.

Acknowledgements

The original source of the data was provided by CETESB.

Inspiration

Explore the data and check if we can see improvements in the sanitary sewer system over the years. Does the weather, tidal variance and holidays/vacations impact in the water quality of the beaches? How? What are other factors that can influence the water quality? Can we cross this data with other environment/development/etc data sources and see what can be done to improve our beaches?

Versions

Version 3 and 4

Updated data to complete 2020. Due to Covid-19 CETESB didn't take measures in many of the dates and beaches.

Some collection points presented a change in the name, and it was considered as below:

São Sebastião * Maresias Praça do Surf: Maresias * Maresias Travessa 15: Maresias - Totem * 'Preta do Norte' has two inputs, one of them was considered 'Preta' (considering the previous data).

Itanhaém * Jardim Suarão: Suarão - Afpesp

Version 5

Updated with data until 2021-07-19.

The reports from CETESB don't contain measurements between 2021-03-15 and 2021-04-12.

The objective of this study was to evaluate the influence of the spacing of lettuce rows on the production of a lettuce-rocket intercropping system over two growing seasons (11 August to 25 September 2011 and 12 January to 24 February 2012) in Jaboticabal, São Paulo, Brazil. We evaluated 11 treatments in each season: lettuce-rocket intercrops with five row spacings for the lettuce (0.20, 0.25, 0.30, 0.35 and 0.40 m) and the rocket planted midway between the lettuce rows, sole crops of lettuce at the same five row spacings and a sole crop of rocket. Fresh and dry masses of the lettuce and rocket and number of lettuce leaves per plant were highest with a lettuce row spacing of 0.40 m, but the productivities of the lettuce and rocket were higher with a lettuce row spacing of 0.20 m. The productivities and fresh and dry weights of the lettuce and rocket and the number of lettuce leaves per plant were highest in the sole crops, but the fresh and dry weights of the rocket were higher with intercropping. The land equivalent ratios were >1.0 in both seasons in all intercrops and were highest for the densest crop (1.41). Intercropping was therefore 41% more efficient than sole cropping for the production of lettuce and rocket.

The Brazil cold chain logistics market, valued at $2.67 billion in 2025, is poised for robust growth, exhibiting a Compound Annual Growth Rate (CAGR) of 10.02% from 2025 to 2033. This expansion is fueled by several key drivers. The burgeoning food processing industry, particularly within the horticulture (fresh fruits and vegetables), meats, fish, and poultry sectors, necessitates efficient cold chain solutions for maintaining product quality and extending shelf life. Rising consumer demand for fresh and high-quality food products further fuels market growth. Furthermore, the pharmaceutical and life sciences sectors are contributing significantly, demanding stringent temperature-controlled logistics for sensitive medications and biological samples. The expansion of e-commerce and the increasing adoption of temperature-sensitive products delivered directly to consumers are also contributing factors. Growth is concentrated in key cities like Sao Paulo, Rio de Janeiro, and Salvador, reflecting higher population density and consumption patterns. While challenges remain, such as maintaining infrastructure and addressing regulatory hurdles, the overall market outlook is positive, driven by increasing investment in modern cold storage facilities and logistics technology. The market segmentation reveals significant opportunities across various services (storage, transportation, value-added services), temperature types (chilled, frozen), and applications. Growth in the frozen segment is expected to be particularly strong, driven by the rising popularity of frozen foods and the need for efficient handling of temperature-sensitive products. Key players like Superfrio Armazens Gerais Ltda, Maersk, Logfrio SA, and others are actively investing in capacity expansion and technological advancements to cater to the rising demand. The strategic partnerships between logistics providers and food producers are paving the way for integrated and efficient cold chain solutions, improving overall supply chain resilience and reducing losses. The continuous improvement in cold chain infrastructure, including refrigerated trucking and warehousing, is vital to sustaining the market's projected growth trajectory. This in-depth report provides a comprehensive analysis of the Brazil cold chain logistics market, offering invaluable insights for businesses operating within or planning to enter this dynamic sector. With a study period spanning 2019-2033, a base year of 2025, and a forecast period of 2025-2033, this report leverages historical data (2019-2024) to project future market trends and growth opportunities. The report covers key segments, including storage, transportation, and value-added services, across various temperature types (chilled and frozen) and applications (horticulture, meats, pharmaceuticals, and more) in major cities like Sao Paulo, Rio de Janeiro, and Salvador. The market is valued in millions and analyzes the impact of regulations, competition, and technological advancements. Key drivers for this market are: The Growth of Banking and Financial Institutions in Emerging Economies, Mobile Payments are Being Increasingly Used. Potential restraints include: Increasing Usage of Payments from Mobile. Notable trends are: Increasing Meat Exports to Drive the Market.

Aedes aegypti is one of the species most favored by changes in the environment caused by urbanization. Its abundance increases rapidly in the face of such changes, increasing the risk of disease transmission. Previous studies have shown that mosquito species that have adapted to anthropogenic environmental changes benefit from urbanization and undergo population expansion. In light of this, we used microsatellite markers to explore how urbanization processes may be modulating Ae. aegypti populations collected from three areas with different levels of urbanization in the city of São Paulo, Brazil. Specimens were collected at eleven sites in three areas with different degrees of urbanization in the city of São Paulo: conserved, intermediate and urbanized. Ten microsatellite loci were used to characterize the populations from these areas genetically. Our findings suggest that as urbanized areas grow and the human population density in these areas increases, Ae. aegypti populations undergo a major population expansion, which can probably be attributed to the species’ adaptability to anthropogenic environmental changes. Our findings reveal a robust association between, on the one hand, urbanization processes and densification of the human population and, on the other, Ae. aegypti population structure patterns and population expansion. This indicates that this species benefits from anthropogenic effects, which are intensified by migration of the human population from rural to urban areas, increasing the risk of epidemics and disease transmission to an ever-increasing number of people.

Background: Improper antibiotic use is one of the main drivers of bacterial resistance to antibiotics, increasing infectious diseases morbidity and mortality and raising costs of healthcare. The level of antibiotic consumption has been shown to vary according to socioeconomic determinants (SED) such as income and access to education. In many Latin American countries, antibiotics could be easily purchased without a medical prescription in private pharmacies before enforcement of restrictions on over-the-counter (OTC) sales in recent years. Brazil issued a law abolishing OTC sales in October 2010. This study seeks to find SED of antibiotic consumption in the Brazilian state of São Paulo (SSP) and to estimate the impact of the 2010 law. Methods: Data on all oral antibiotic sales having occurred in the private sector in SSP from 2008 to 2012 were pooled into the 645 municipalities of SSP. Linear regression was performed to estimate consumption levels that would have occurred in 2011 and 2012 if no law regulating OTC sales had been issued in 2010. These values were compared to actual observed levels, estimating the effect of this law. Linear regression was performed to find association of antibiotic consumption levels and of a greater effect of the law with municipality level data on SED obtained from a nationwide census. Results: Oral antibiotic consumption in SSP rose from 8.44 defined daily doses per 1,000 inhabitants per day (DID) in 2008 to 9.95 in 2010, and fell to 8.06 DID in 2012. Determinants of a higher consumption were higher human development index, percentage of urban population, density of private health establishments, life expectancy and percentage of females; lower illiteracy levels and lower percentage of population between 5 and 15 years old. A higher percentage of females was associated with a stronger effect of the law. Conclusions: SSP had similar antibiotic consumption levels as the whole country of Brazil, and they were effectively reduced by the policy.

Abstract In 2015, a serious environmental disaster occurred at ULTRACARGO - Aratu S/A Terminal (Cubatão, SP) causing a long-lasting, large-scale, fire that resulted in the release of various chemical pollutants, including those used to contain the fire. These pollutants affected adjacent regions and the innermost area of the Santos-São Vicente Estuarine System, requiring the assessment of environmental quality in two mangrove areas post-disaster (2016). This assessment considered biomarkers for the species including population density, structure, and cytogenotoxicity. The population structure and cytotoxicity of Ucides cordatus (Linnaeus, 1763) only changed slightly from pre-disaster (2013) to post-disaster (2016), as a consequence of the greater resilience and biological flexibility of this crab to environmental stress caused by pollutants. We recommend continuous monitoring be conducted using this species endemic to the mangroves of the study site, as this will make it possible to assess the magnitude of the chronic environmental impacts of the accident. In addition, it could guide environmental agencies in damage mitigation or in the quantification of possible future impacts.

In 2023, SuperFrio expanded its footprint in the Southeastern region of Brazil by opening a new 20,000-pallet-position facility in São Paulo, targeting temperature-sensitive food and pharmaceutical products. São Paulo and Rio de Janeiro are key cold chain markets due to their proximity to ports, high population density, and well-established industrial and retail clusters. The Brazil cold chain market reached a valuation of BRL 26.4 Billion in 2023, driven by rising demand for perishable food products, expansion in the pharmaceutical and vaccine sectors, and increasing investments in cold storage infrastructure. The market is characterized by key players such as SuperFrio, Comfrio, Arfrio, Friozem Armazéns Frigoríficos, and Solistica. These companies are known for their extensive cold storage facilities, last-mile delivery capabilities, and integrated logistics networks. Brazil Cold Chain Market Overview and Size

Определение: Предполагаемое число постоянных жителей в географической единице пропорционально площади этой единицы. [Переведено с en: английского языка] Тематическая область: База данных городов [Переведено с en: английского языка] Область применения: Общественный [Переведено с en: английского языка] Единица измерения: Количество жителей на квадратный километр (кв. км) [Переведено с en: английского языка] Примечание: Эта база данных была подготовлена при содействии Европейского союза в рамках программы EUROCLIMA (CEC/14/001). Ответственность за ее содержание несет исключительно ЭКЛАК. [Переведено с es: испанского языка] Источник данных: Оценка численности населения - Бразильский институт географии и статистики - IBGE [Переведено с es: испанского языка] Последнее обновление: Feb 15 2019 8:29PM Организация-источник: Фонд Seade, правительство штата Сан-Паулу [Переведено с en: английского языка] Definition: Estimated number of resident inhabitants in the geographical unit prorated to the area of this unit. Thematic Area: Database cities Application Area: Social Unit of Measurement: Inhabitant per square kilometer (sq km) Note: This database has been produced with the assistance of the European Union, through EUROCLIMA Program (CEC/14/001). The contents are the sole responsibility of ECLAC. Data Source: Population estimates - Brazilian Institute of Geography and Statistics - IBGE Last Update: Feb 15 2019 8:29PM Source Organization: Seade Foundation, Government of the State of Sao Paulo

ABSTRACT Background: Chagas disease (CD) is caused by the flagellate protozoan Trypanosoma cruzi and can be carried by different species of triatomines, including Rhodnius neglectus, which is wild, well distributed in Brazil, and has formed colonies in palm trees located in urban areas of municipalities in the state of São Paulo. Chemical control has been routinely used to reduce population density, but each year, there has been an increase in species dispersion and density. This study aimed to evaluate the susceptibility of insects to insecticides used in control. Methods: The reference population was collected from Araçatuba municipality, Nilce Maia. Dilutions of deltamethrin were prepared and applied to the back of the first-stage nymphs, which were biologically synchronized. The control group received pure acetone only. Mortality was assessed after 72 h. Results: The mortality rate with respect to diagnostic dose was 100%. The susceptibility profile observed for this population showed RR50 ranging from 1.76 to 3.632. Conclusions: The populations were susceptible to the insecticides tested. It is possible that the insecticide residual effect on this ecotope has decreased the lifespan, and controlling failures may be the cause of recolonization in this environment.

This file collection contains the estimated spatial distribution of the above-ground biomass density (AGB) by the end of the 21st century across the Brazilian Atlantic Forest domain and the respective uncertanty. To develop the models, we used the maximum entropy method with projected climate data to 2100, based on the Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) 4.5 from the fifth Assessment Report (AR5).

The dataset is composed of four files in GeoTIFF format:

calibrated-AGB-distribution.tif: raster file representing the present spatial distribution of the above-ground biomass density in the Atlantic Forest from the calibrated model. Unit: Mg/ha

estimated-uncertanty-for-calibrated-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the calibrated above-ground biomass density. Unit: percentage.

projected-AGB-distribution-under-rcp45.tif: raster file representing the projected spatial distribution of the above-ground biomass density in the Atlantic Forest by the end of 2100 under RCP 4.5 scenario. Unit: Mg/ha

estimated-uncertanty-for-projected-agb-distribution.tif: raster file representing the estimated spatial uncertanty distribution of the projected above-ground biomass density. Unit: percentage.

Spatial resolution: 0.0083 degree (ca. 1 km)

Coordinate reference system: Geographic Coordinate System - Datum WGS84

ABSTRACT This study aims to relate information about the waiting times of ride-sourcing services, with specific reference to Uber, using socioeconomic variables from São Paulo, Brazil. The intention is to explore the possibility of using this measure as an accessibility proxy. A database was created with the mean waiting time data per district, which was aggregated to a set of socioeconomic and transport infrastructure variables. From this database, a multiple linear regression model was built. In addition, the stepwise method selected the most significant variables. Moran's I test confirmed the spatial distribution pattern of the measures, motivating the use of a spatial autoregressive model. The results indicate that physical variables, such as area and population density, are important to explain this relation. However, the mileage of district bus lines and the non-white resident rate were also significant. Besides, the spatial component indicates a possible relation to accessibility.