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Introdução à análise geoespacial
Em um data warehouse como o BigQuery, as informações de localização são comuns e podem influenciar decisões comerciais importantes. Você pode usar a análise geoespacial para analisar e visualizar dados geoespaciais no BigQuery usando o tipo de dados GEOGRAPHY e as funções geográficas do GoogleSQL.
Por exemplo, é possível registrar a latitude e a longitude dos veículos de entrega ou dos pacotes ao longo do tempo. Também é possível registrar transações de clientes e mesclar os dados de outra tabela com os dados de local de armazenamento. É possível usar esse tipo
de dados de local para fazer o seguinte:
Estimar quando um pacote vai chegar.
Determinar quais clientes devem receber uma encomenda de uma loja específica.
Combine seus dados com a porcentagem de cobertura arbórea das imagens de satélite para decidir se a entrega por drone aéreo é viável.
Limitações
A análise geoespacial está sujeita às seguintes limitações:
Apenas a biblioteca de cliente do BigQuery para Python é compatível com
o tipo de dados GEOGRAPHY. Para outras bibliotecas de cliente, converta valores GEOGRAPHY
em strings usando a função ST_ASTEXT ou ST_ASGEOJSON.
A conversão para texto usando ST_ASTEXT armazena apenas um valor.
Já a conversão para WKT faz com que os dados sejam anotados como STRING em vez
de GEOGRAPHY.
Cotas
As cotas e os limites na análise geoespacial se aplicam aos diferentes tipos de jobs executados em tabelas que contêm dados geoespaciais, incluindo os seguintes tipos de jobs:
Muitas operações de tabelas são gratuitas, incluindo o carregamento de dados, a cópia de tabelas e a exportação de dados. Por isso, elas estão sujeitas às cotas e limites do BigQuery. Para mais informações,
consulte Operações gratuitas na
página de preços.
[[["Fácil de entender","easyToUnderstand","thumb-up"],["Meu problema foi resolvido","solvedMyProblem","thumb-up"],["Outro","otherUp","thumb-up"]],[["Difícil de entender","hardToUnderstand","thumb-down"],["Informações incorretas ou exemplo de código","incorrectInformationOrSampleCode","thumb-down"],["Não contém as informações/amostras de que eu preciso","missingTheInformationSamplesINeed","thumb-down"],["Problema na tradução","translationIssue","thumb-down"],["Outro","otherDown","thumb-down"]],["Última atualização 2025-08-17 UTC."],[[["\u003cp\u003eGeospatial analytics in BigQuery allows for the analysis and visualization of location data, utilizing geography data types and GoogleSQL geography functions.\u003c/p\u003e\n"],["\u003cp\u003eLocation data, such as latitude and longitude, is commonly used in data warehouses to inform critical business decisions, like delivery times or targeted marketing.\u003c/p\u003e\n"],["\u003cp\u003eGeospatial analytics has some limitations, including being exclusively available in GoogleSQL and with the BigQuery client library for Python being the only one to directly support the \u003ccode\u003eGEOGRAPHY\u003c/code\u003e data type.\u003c/p\u003e\n"],["\u003cp\u003eThe use of geospatial analytics in BigQuery incurs costs based on data storage and query execution, with certain operations like loading, copying, and exporting data being free, but still subject to quotas and limits.\u003c/p\u003e\n"],["\u003cp\u003eSeveral resources are available for those wishing to learn more, including getting started guides, visualization options, and information on working with geospatial data and GoogleSQL functions.\u003c/p\u003e\n"]]],[],null,["# Introduction to geospatial analytics\n====================================\n\nIn a data warehouse like BigQuery, location information is\ncommon and can influence critical business decisions. You can use geospatial\nanalytics to analyze and visualize geospatial data in BigQuery\nby using the\n[`GEOGRAPHY` data type](/bigquery/docs/reference/standard-sql/data-types#geography_type)\nand\n[GoogleSQL geography functions](/bigquery/docs/reference/standard-sql/geography_functions).\n\nFor example, you might record the latitude and longitude of your delivery\nvehicles or packages over time. You might also record customer transactions and\njoin the data to another table with store location data. You can use this type\nof location data to do the following:\n\n- Estimate when a package is likely to arrive.\n- Determine which customers should receive a mailer for a particular store location.\n- Combine your data with percent tree cover from satellite imagery to decide if delivery by aerial drone is feasible.\n\nLimitations\n-----------\n\nGeospatial analytics is subject to the following limitations:\n\n- [Geography functions](/bigquery/docs/reference/standard-sql/geography_functions) are available only in GoogleSQL.\n- Only the BigQuery client library for Python supports the `GEOGRAPHY` data type. For other client libraries, convert `GEOGRAPHY` values to strings by using the `ST_ASTEXT` or `ST_ASGEOJSON` function. Converting to text using `ST_ASTEXT` stores only one value, and converting to WKT means that the data is annotated as a `STRING` type instead of a `GEOGRAPHY` type.\n\nQuotas\n------\n\nQuotas and limits on geospatial analytics apply to the different types of\njobs you can run against tables that contain geospatial data, including the\nfollowing job types:\n\n- [Loading data](/bigquery/quotas#load_jobs) (load jobs)\n- [Exporting data](/bigquery/quotas#export_jobs) (export jobs)\n- [Querying data](/bigquery/quotas#query_jobs) (query jobs)\n- [Copying tables](/bigquery/quotas#copy_jobs) (copy jobs)\n\nFor more information on all quotas and limits, see [Quotas and limits](/bigquery/quotas).\n\nPricing\n-------\n\nWhen you use geospatial analytics, your charges are based on the\nfollowing factors:\n\n- How much data is stored in the tables that contain geospatial data\n- The queries you run against the data\n\nFor information on storage pricing, see [Storage pricing](/bigquery/pricing#storage).\n\nFor information on query pricing, see [Analysis pricing models](/bigquery/pricing#analysis_pricing_models).\n\nMany table operations are free, including loading data, copying tables, and\nexporting data. Though free, these operations are subject to\nBigQuery's [Quotas and limits](/bigquery/quotas). For information\non all free operations, see [Free operations](/bigquery/pricing#free) on the\npricing page.\n\nWhat's next\n-----------\n\n- To get started with geospatial analytics, see [Get started with geospatial analytics](/bigquery/docs/geospatial-get-started).\n- To learn more about visualization options for geospatial analytics, see [Visualize geospatial data](/bigquery/docs/geospatial-visualize).\n- To learn more about working with geospatial data, see [Work with geospatial data](/bigquery/docs/geospatial-data).\n- To learn more about working with raster data, see [Work with raster data](/bigquery/docs/raster-data).\n- To learn more about incorporating Google Earth Engine geospatial data into BigQuery, see [Load Google Earth Engine geospatial data](/bigquery/docs/geospatial-data#load-ee).\n- For documentation on GoogleSQL functions in geospatial analytics, see [Geography functions in GoogleSQL](/bigquery/docs/reference/standard-sql/geography_functions).\n- To learn about different grid systems, see [Grid systems for spatial analysis](/bigquery/docs/grid-systems-spatial-analysis).\n- To learn more about geospatial datasets and geospatial analytics and AI, see [Geospatial Analytics](/solutions/geospatial)."]]
