How would you address discrepancies between field data and GIS database information?

Suele ocurrir cuando los gps no se encuentran calibrados o los datos provienen de diferenctes fuentes, se recomienda revisarun rango de coordenadas dentro de los cuales deberian de estar los sistemas de refencia mas comun para poder emplear las herramientas de Definir y Proyectar coordenadas en un msmo shape o sistema de referencia

You also need to think about temporal differences (old data may no longer reflect today’s reality), the skills, equipment, procedures and data sources of the people who collected the older and newer data sources (students vs experienced professionals, digitized maps vs GPS, etc) and the original purpose of the data. There is always a trade off between budget and data quality. There’s no point collecting data with a higher quality standard than the stated purpose. If you then try to ‘push’ data to a higher quality than intended you will have issues.

Recomendo o uso das ferramentas do ArcGIS: Collector ou Survey123 para coletar dados de campo de forma precisa e padronizada. Posteriormente, utilize o ArcGIS Pro para realizar a integração e comparação dos dados de campo com o banco de dados GIS. Ferramentas como Data Reviewer podem ser empregadas para automatizar a validação e a correção de erros, reduzindo significativamente o retrabalho e garantindo a consistência e a integridade dos dados.

Revisão de Dados de Campo vs. Banco de Dados SIG Coleta e Pré-processamento Reúna dados de campo e SIG. Normalize formatos e verifique projeções e datums. Comparação e Identificação de Discrepâncias Sobreponha dados no software GIS. Calcule diferenças e compile estatísticas de desvios. Análise e Diagnóstico Identifique padrões de discrepâncias: sistemáticos ou aleatórios. Verifique compatibilidade de projeções e datums. Ajustes e Correções Transforme projeções e ajuste datums conforme necessário. Reavalie discrepâncias após ajustes. Documentação Documente todas as etapas e prepare um relatório final. Ferramentas QGIS/ArcGIS: Para análise de dados. Excel/Google Sheets: Para estatísticas básicas. R/Python: Para análise avançada.

Typically, we require certain accuracy and precision in our data to make geospatial inferences. Depending on the intended outcome of the geospatial assessment, we may accept a certain margin of error, as long as we can correct our Interpretation of the data appropriately. Discrepancies between field data and remote sensing data may originate at various sources. One can have the best earth observation dataset but are collecting field data on an inaccurate mobile device. Or, have an accurate GPS device, but assessing the data on an outdated 2D dataset. It help to ensure that your ground truthing devices (GPS, Cellular Phone), your earth observation dataset and your software correspond to your intended accuracy and precision.

Pontos de Atenção Dados Desatualizados: Discrepâncias podem surgir de dados antigos. Diferenças Metodológicas: Variações nas metodologias de coleta podem causar inconsistências. Importância Garantir que ambas as fontes sejam confiáveis e atualizadas é essencial para manter a integridade dos dados GIS.

It is critical to verify the sources of both data sets. The reliability and timeliness of the sources can significantly affect the accuracy of the data. It is very relevant to always ensure that both field data and GIS database data came from verified, high-quality sources, such as national censuses and statistics. This verification allows to have confidence in the integrity of the data you work with.

This is something I feel we should all strive for - ensuring that information that has been gathered has not only been correctly and accurately recorded, but also who, when, how has also been recorded with this information to help others understand if there are any shortcomings or issues.

Verifying sources is crucial for GIS system. It involves certain processes like source evaluation, metadata examination, cross referencing of data, data sampling and documentation review. Remember that thorough source verification ensures the reliability of your data. If you encounter any inconsistencies, investigate further to resolve them.

Use the most up to date data will allow for current information. This allows for data integrity to be both accurate and correct information within databases.

If you can easily get to the field, then go there first and take your GIS data with you. You can confront the data on the spot if you are competent in the matter. The field is the reality by excellence. Depending on the problem you can take new samples and have them examined. Calibration is a most important issue for example when calculating sea surface temperature from satellite data. If your calibration is not excellent your maps will be wrong.

Check the methods used to collect field data to ensure they are consistent with the existing database. Identify any potential sources of error, such as differences in coordinate systems, datum, or spatial reference systems.

In situ validation is a crucial stage where data are checked in the field. This involves making visits to the data collection sites to ensure that the information captured in the field matches the observed reality. In situ validation is essential, especially in urban infrastructure and transportation network analysis. These validations make it possible to correct erroneous data and improve the accuracy of the databases.

Keep in mind how the data were collected. GPS (unless using a geo-referenced home base) commonly has a maximum accuracy of +/- 5m under open skies, and greater error under canopy on along very winding routes (sequential point data). The age of aerial photos can affect accuracy as well as the angle of photography. How experienced the field operator is, and how well the requirements were explained can also change accuracy.

Benefícios da Validação no Local Precisão Melhorada: Obter uma imagem mais precisa e confiável dos dados. Entendimento do Terreno: Compreensão mais profunda das condições locais e possíveis causas de discrepâncias. Resumo A validação no local é uma etapa crucial para resolver discrepâncias não resolvidas entre dados de campo e o banco de dados SIG, melhorando a precisão e a confiabilidade dos dados GIS.

Establishing clear and consistent update protocols is essential to maintaining the integrity of the GIS database. This includes defining procedures for the collection, verification, and entry of new or corrected data. It is vital that any new or updated data is consistently and uniformly integrated into each project, avoiding future discrepancies and improving the quality of spatial analyses.

One of the keys that I find for this is making sure that the process is well understood and well documented (PFD's are great!). We also typically add a few additional fields to the data set to record who made the edit, when, what type of edit, and any particulars (i.e. why, data source) so that, if necessary, we can roll it back.

Updating protocols includes drafting clear Standard Operating Procedures (SOPs) for data review, on-site validation and source verification. You should include step-by-step instructions, roles, responsibilities and quality control measures in the SOP. Involving stakeholders like your company's staff, GIS analysts, domain experts, etc in protocol development, that would be a good suggestion. Regularly communicate updates and changes to ensure everyone follows the latest procedures. Create checklists for field inspections and data collection, use standarized forms to record observations, attribute data and metadata, implement quality assurance checks at various stages during data collection, after data entry and before database updates.

Correcting GIS data using validated field data and updated protocols is crucial. Utilizing GIS tools to adjust attribute information, spatial boundaries, or conflicting data ensures accurate representation in GIS databases. Documentation and version control processes ensure transparency and accountability in data revisions

Ensuring accurate and precise GIS data is crucial to avoid discrepancies between field data and GIS database. You should ensure that the GIS data is accurate and closely matches to real-world values. It should be precise and should describe how exact the data description is. Precise data may be inaccurately gathered due to surveyors errors or recording mistakes. For example: Image A: Precise and accurate. Image B: Precise but not accurate. Image C: Accurate but imprecise. Image D: Neither accurate nor precise. To minimise errors, use multiple data sources, validate and cross-check data, be transparent about limitations or assumptions in your analysis and leverage metadata to understand data quality and limitations.

Once discrepancies have been identified and validated, the next step is to correct the data in the GIS database. This may involve modifying coordinates, attributes or even deleting incorrect data. Tools such as QGIS and R to perform these corrections are efficient and accurate. For example, during a transportation demand analysis project, it is common to have to correct several errors in the database, which significantly improves the results of the demand model.

Maintaining the integrity of the GIS database is an ongoing task. This involves constantly monitoring the quality of the data and performing periodic audits to identify and correct potential errors. It is crucial to implement quality control systems that ensure that the data remains accurate and reliable over time. These systems include detailed documentation of all changes made and training of staff in good data handling practices.

To maintain the integrity of your GIS database, implement regular checks such as periodic field validations and cross-referencing with reliable data sources. Stay updated on new methodologies and technologies, conduct routine quality assurance practices like automated checks and data audits, and ensure thorough documentation and reporting. These steps will help minimize discrepancies and keep your GIS data accurate and reliable.

●Verify and validate: Double-check your field data gathering methods to confirm that your measurements are accurate. Then, check the GIS data source and lineage to determine any potential limits. ●Reconcile Discrepancies: If discrepancies remain, try to resolve them by returning to the field or examining historical records. The GIS database can also be updated with confirmed field data. ●Document Findings: Keep track of any differences and the steps you took to remedy them. This will be useful for future reference and data quality enhancements.

Define the objective and parameters you want from GIS capture Consider the accuracy required from GIS data you have been provided