From 0a9449a73c2a93e1579733a6d0c09feb5fc52f55 Mon Sep 17 00:00:00 2001 From: dragosgontariu Date: Sat, 24 Aug 2024 13:12:47 +0000 Subject: [PATCH] deploy: 550913987f6adc91f2301bf171acfe87135bddc6 --- README.html | 54 +++++++++++++++++++++------------------------- _sources/README.md | 8 +++---- searchindex.js | 2 +- 3 files changed, 30 insertions(+), 34 deletions(-) diff --git a/README.html b/README.html index a7f399b3..f5f28aea 100644 --- a/README.html +++ b/README.html @@ -413,13 +413,6 @@

Contents

  • 2. Geospatial Databases
  • 3. Novel Analytical Framework
  • 4. Gender Enabling Environments Tool (GEEST)
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  • 6. GEEST Main Limitations -
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    Place characterization factors aggregated

    Below are some of the overall insights derived from the GEEST for Saint Lucia. The first map illustrates the degree of enablement within environments; the second map shows the level of enablement concerning the female population on a raster 100x100m format. The third map shows the results for the level of enablement concerning female population, aggregated at the administrative subdivisions of the island.

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    Raster results concerning enabling environments for Saint Lucia

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    Raster results concerning enabling environments with respect to women’s population for Saint Lucia

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    Aggregate results concerning enabling environments with respect to women’s population for Saint Lucia at administrative subdivision level

    If the job opportunities’ location layer is presented in raster format, the GEEST can generate scores specifically for the regions where employment prospects exist, emphasizing opportunities exclusively within those areas.

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    6. GEEST Main Limitations#

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    Data Availability and Granularity#

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    Raster results concerning enabling environments in RE sites with respect to women’s population for Saint Lucia

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    6. GEEST Main Limitations

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    Data Availability and Granularity

    The effectiveness of GEEST relies heavily on the quality of open-source data. Therefore, it is imperative to underscore that the principal limitation of the tool in its current state originates from the scarcity of open-source data in SIDS and the limited granularity of the available data. One of the primary challenges in data collection for St. Lucia was the prevalence of information in non-geospatial formats, such as PDFs and JPGs, or as simple statistics found in sources like the World Bank’s Data Portal. Specifically, the Central Statistical Office of St. Lucia’s Mapping Unit distributes maps exclusively in PDF format. Additionally, at the time of our research, the 2022 Census data had not yet been released, leaving us with only the 2010 Census data, which may not accurately reflect the current situation of the country. Furthermore, this earlier census data was largely unavailable in a geospatial format. Moreover, as mentioned earlier, information sourced from open-source repositories (like OSM) was occasionally outdated, incomplete, or lacked detailed specifications regarding the attributes of mapped features, a notable example being financial facilities.

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    Processing Time#

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    Processing Time

    The processing of factors within the Place Characterization dimension is notably slow. Subsequent enhancements should prioritize the reduction of computational demands for calculating these indicators.

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    Methodological Deviations#

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    Methodological Deviations

    The Place Characterization factors pose challenges due to restricted data availability, necessitating deviations from the proposed geoprocessing techniques outlined in the methodology. The granularity of GEEST’s output is confined by available data, suggesting potential refinement opportunities in geoprocessing procedures with improved data access or alternative methods to those specified in the methodology.

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    Opportunities for Enhancement#

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    Opportunities for Enhancement

    To improve the quality and availability of data for GEEST input in Saint Lucia, the following enhanced strategies are recommended:

    Invest in Proprietary Gender Data: Secure proprietary datasets that shed light on domestic violence, Gender-Based Violence (GBV), and women’s safety perceptions. Collaborate with organizations specializing in gender-sensitive issues, as they likely possess valuable data.

    Leverage Satellite Imagery and Drone Technology: Use satellite imagery and aerial photography to assess the comprehensiveness and quality of the geospatial datasets included in this analysis. Specifically, around the areas where potential or current job opportunities are located, to verify the accuracy of the results.

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    Opportunities for Enhancement @@ -851,13 +854,6 @@

    Opportunities for Enhancement2. Geospatial Databases
  • 3. Novel Analytical Framework
  • 4. Gender Enabling Environments Tool (GEEST)
    • -
  • 6. GEEST Main Limitations -
    • diff --git a/_sources/README.md b/_sources/README.md index 58296a08..e9b3048d 100644 --- a/_sources/README.md +++ b/_sources/README.md @@ -245,7 +245,7 @@ The Place-Characterization dimension contains factors that refer to attributes t Below are some of the overall insights derived from the GEEST for Saint Lucia. The first map illustrates the degree of enablement within environments; the second map shows the level of enablement concerning the female population on a raster 100x100m format. The third map shows the results for the level of enablement concerning female population, aggregated at the administrative subdivisions of the island. -:::{figure-md} markdown-fig +

    picture

    @@ -253,7 +253,7 @@ Below are some of the overall insights derived from the GEEST for Saint Lucia. T Raster results concerning enabling environments for Saint Lucia ::: -:::{figure-md} markdown-fig +

    picture @@ -262,7 +262,7 @@ Raster results concerning enabling environments for Saint Lucia Raster results concerning enabling environments with respect to women’s population for Saint Lucia ::: -:::{figure-md} markdown-fig +

    picture @@ -273,7 +273,7 @@ Aggregate results concerning enabling environments with respect to women’s pop If the job opportunities' location layer is presented in raster format, the GEEST can generate scores specifically for the regions where employment prospects exist, emphasizing opportunities exclusively within those areas. -:::{figure-md} markdown-fig +

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Gender Spatial Data Gap Assessment": [[0, "gender-spatial-data-gap-assessment"]], "2. Geospatial Databases": [[0, "geospatial-databases"]], "3. Novel Analytical Framework": [[0, "novel-analytical-framework"]], "4. Gender Enabling Environments Tool (GEEST)": [[0, "gender-enabling-environments-tool-geest"]], "Proposed discrete score classes to enable simpler visual interpretation of raw score outputs and enable intersection with other layers of information (reproduced from the Methodology Report).": [[0, "class-scores-table"]], "6. 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