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UID:pretalx-foss4g-2024-LZHCDP@talks.staging.osgeo.org
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DESCRIPTION:In today's fast-paced\, data-driven environment\, organizations
  that use geospatial data for analysis are challenged by managing complex 
 datasets and frequent updates. Geospatial data provides valuable context a
 nd information\, enhancing applications in various domains such as logisti
 cs\, urban planning\, environmental monitoring\, and marketing.\nTradition
 ally\, many organizations have relied on no-code geospatial software with 
 click-based interfaces. Due to their accessibility and user-friendly inter
 face\, these tools allow team members to visualize and manipulate geospati
 al data without the need for programming knowledge. However\, as data star
 ts to become more complex\, these tools often present scalability limitati
 ons\, restricting the full potential of geospatial data applications.\nThi
 s paper explores the benefits of transitioning to a hybrid approach by int
 egrating Python and open-source geospatial libraries into the data process
 ing phase of geospatial analysis. By presenting the possible advantages ga
 ined and providing a hands-on example of Python use in geospatial data\, t
 he aim of this paper is to demonstrate how Python can play a pivotal role 
 in overcoming the limitations of no-code solutions.\nPython can enhance th
 e data extraction phase\, enabling integration with various data sources a
 nd APIs and connecting to external databases and web services. This capabi
 lity supports consistent data exchange and real-time data integration. Thi
 s phase can also be automated\, summarizing all steps into a script that c
 an be applied to every new dataset.\nThe processed data can be visualized 
 using various libraries in a Python environment or used as input for tradi
 tional geospatial software. The hybrid approach leverages the user-friendl
 y visualization tools of no-code software while enabling more sophisticate
 d data processing capabilities.\nThe output data can also be used as input
  for developing custom algorithms\, including the integration of machine l
 earning models and artificial intelligence. This step enables a wide range
  of applications\, such as urban feature prediction\, classification or se
 gmentation of remote sensing data\, and clustering of spatial data.\nAdopt
 ing a hybrid approach significantly enhances an organization's analytical 
 capabilities. These advanced analyses provide deeper insights into spatial
  patterns and trends that manual methods alone may not reveal.\nThe hands-
 on example\, based on census data from the Brazilian Institute of Geograph
 y and Statistics (IBGE)\, demonstrates geospatial data processing with Pyt
 hon and the GeoPandas library\, both open-source solutions. This example w
 ill include the use of Python in geospatial data processing through the fo
 llowing steps: data extraction\, data processing\, and customized algorith
 m application.\nIn conclusion\, integrating Python into these workflows en
 hances flexibility and analytical capabilities\, allowing organizations to
  innovate in their solutions and create new opportunities for products and
  services. Coding elevates data-driven decision-making and enables more so
 phisticated and scalable analyses\, particularly when dealing with large a
 nd complex datasets. This case study serves as an inspiring example for or
 ganizations and researchers aiming to maximize the potential of their geos
 patial data\, highlighting the significant benefits of combining tradition
 al geospatial software with powerful open-source tools.\n\nThis work recei
 ved financial support from the State of São Paulo Research Foundation (FA
 PESP) (grant 2023/15663-7\, 2024/05553-2\, 2024/05727-0\, 2024/05481-1).
DTSTAMP:20260505T233321Z
LOCATION:Room V
SUMMARY:Enhancing Geospatial Data Processing with Python: A Case Study usin
 g IBGE data - Ana Beatriz de Figueiredo Oliveira
URL:https://talks.staging.osgeo.org/foss4g-2024/talk/LZHCDP/
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