This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

Urban Reference Of The Municipality Of Sao Paulo
This dataset falls under the category Planning & Policy Other.
It contains the following data: This dataset contains the perimeters of urban references in the municipality (airports, main museums, universities, hospitals, markets, stadiums, clubs and others) at the lot level. Urban references were demarcated through satellite images provided by the Google Earth program. Subsequently, the perimeters were adjusted according to the lots made available by the Digital City Map (MDC).
This dataset was scouted on 2022-02-10 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: http://dados.prefeitura.sp.gov.br/dataset/referencia-urbana-do-municipio-de-sao-paulo

The objective of the Mapa 3D Digital da Cidade (M3DC) of the São Paulo City Hall is to publish LiDAR point cloud data. The initial data was acquired in 2017 by aerial surveying and future data will be added. This publicly accessible dataset is provided in the Entwine Point Tiles format as a lossless octree, full density, based on LASzip (LAZ) encoding.

This dataset is intended for researchers, students, and policy makers for reference and mapping purposes, and may be used for basic applications such as viewing, querying, and map output production, or to provide a basemap to support graphical overlays and analysis with other spatial data.

ABSTRACT The following paper aims to deepen the studies about the map of the travel made by D. Luis de Céspedes Xeria from the town of São Paulo de Piratininga to the Ciudad Real de Guayrá, in the Province of Paraguay, in 1628. For this, we used primary sources such as the manuscripts of the De Angelis Collection, kept in the National Library in Rio de Janeiro, as well as the manuscripts of the Archivo General de Indias, in Seville, including the extensive written documentation and, specially, the two available versions of the cartographic document made by Céspedes Xeria, in 1628. We then compared the copy commissioned by Affonso d’Escragnolle Taunay, in 1917, with both of the seventeenth-century remaining originals, concluding for its fidelity to one of them. In addition, we analyzed the cartographic representations of the settlements represented in the map, which aroused, in 1938, public discussions between Taunay and Benedito Carneiro Bastos Barreto, also known as Belmonte, coming up to the conclusion that all of them are cartographic symbols or standardized icons rather than representations based on the appearance of the buildings such as they were when they were visited by Céspedes Xeria in the first half of the seventeenth century. During this process it was possible to distinguish two different moments of the historian Taunay: the first, his concerning for fidelity in copying documents; and the second, a plasticity of interpretation in the moment of analyze and use these same documents.

Our Brazil Zip Code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

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).

It is estimated that more than 8 billion people live on Earth and the population is likely to hit more than 9 billion by 2050. Approximately 55 percent of Earth’s human population currently live in areas classified as urban. That number is expected to grow by 2050 to 68 percent, according to the United Nations (UN).The largest cities in the world include Tōkyō, Japan; New Delhi, India; Shanghai, China; México City, Mexico; and São Paulo, Brazil. Each of these cities classifies as a megacity, a city with more than 10 million people. The UN estimates the world will have 43 megacities by 2030.Most cities' populations are growing as people move in for greater economic, educational, and healthcare opportunities. But not all cities are expanding. Those cities whose populations are declining may be experiencing declining fertility rates (the number of births is lower than the number of deaths), shrinking economies, emigration, or have experienced a natural disaster that resulted in fatalities or forced people to leave the region.This Global Cities map layer contains data published in 2018 by the Population Division of the United Nations Department of Economic and Social Affairs (UN DESA). It shows urban agglomerations. The UN DESA defines an urban agglomeration as a continuous area where population is classified at urban levels (by the country in which the city resides) regardless of what local government systems manage the area. Since not all places record data the same way, some populations may be calculated using the city population as defined by its boundary and the metropolitan area. If a reliable estimate for the urban agglomeration was unable to be determined, the population of the city or metropolitan area is used.Data Citation: United Nations Department of Economic and Social Affairs. World Urbanization Prospects: The 2018 Revision. Statistical Papers - United Nations (ser. A), Population and Vital Statistics Report, 2019, https://doi.org/10.18356/b9e995fe-en.