New docs

docs/Introduction.md

@@ -5,4 +5,4 @@ Over the next four years we will invest in the development and publication of ne
 
 Geographic variables and indicators will be prioritized based on their relevance to economic geography outside high-growth cities across all income categories. The goal is to provide more insight into neglected places, especially second-tier cities and rural areas.  This will provide a more current/accurate understanding of the continuum of urbanization, and the relationship to growth, energy consumption, greenhouse gas (GHG) emissions, industrialization, and public and private investment. 
 
-Where appropriate, the project team will develop a suite of new data products to facilitate this line of work. Out prior experience in the development and deployment of Survey Solutions, NADA plus, The Global Electrification Platform, the renewable energy zoning tool, the Light Every Night database, and the GOSTnets mobility toolset, as well as support to multiple Urbanization reviews and Flagship publications make it the right place in the World Bank to implement the desired program around spatial data disaggregation and survey microdata enhancement. We will rely on the forthcoming Chief Statistician's Data Quality Assurance Framework for data quality checking and metadata standards.
+Where appropriate, the project team will develop a suite of new data products to facilitate this line of work. Our prior experience in the development and deployment of Survey Solutions, NADA plus, The Global Electrification Platform, the renewable energy zoning tool, the Light Every Night database, and the GOSTnets mobility toolset, as well as support to multiple Urbanization reviews and Flagship publications make it the right place in the World Bank to implement the desired program around spatial data disaggregation and survey microdata enhancement. We will rely on the forthcoming Chief Statistician's Data Quality Assurance Framework for data quality checking and metadata standards.

docs/_toc.yml

@@ -19,13 +19,14 @@ parts:
     numbered: False
     chapters:
       - file: sub_tasks/admin_bounds.md
-      - file: sub_tasks/sub_national_welfare.md
       - file: sub_tasks/geest.md
       - file: sub_tasks/geo_enhancement.md
   - caption: Notebook Examples
     numbered: False
     chapters:
       - file: user-docs/space2stats_api_demo.ipynb
       - file: user-docs/space2stats_api_demo_R.md
+      - file: user-docs/space2stats_api_demo_pop_pyramid.ipynb
+      - file: user-docs/space2stats_api_demo_urban_flood_risk.ipynb
       - file: user-docs/space2stats_floods.ipynb
       - file: user-docs/space2stats_py_package_demo.ipynb

docs/readme.md

@@ -25,15 +25,15 @@ The database currently contains four datasets with global coverage:
 
 Population Demographics
 ^^^
-Total Population, 2020, disaggregarated by age and gender. (WorldPop)
+Total Population, 2020, disaggregated by age and gender. (WorldPop)
 :::
 
 :::{grid-item-card}
 :link: https://radiantearth.github.io/stac-browser/#/external/raw.githubusercontent.com/worldbank/DECAT_Space2Stats/refs/heads/main/space2stats_api/src/space2stats_ingest/METADATA/stac/space2stats-collection/urbanization_ghssmod/urbanization_ghssmod.json
 
 Degree of Urbanization
 ^^^
-Population and number of cells in different rural/urban classes. (GHSMOD)
+Population and number of cells in different rural/urban classes. (GHSSMOD)
 :::
 
 :::{grid-item-card}
@@ -395,7 +395,7 @@ PGPASSWORD=
 PGTABLENAME=space2stats
 ```
 
-Connect to the database and use package functions (e.g., `fiels`, `summaries`, `aggregate`). Additional documentation for these is available here. 
+Connect to the database and use package functions (e.g., `fields`, `summaries`, `aggregate`). Additional documentation for these is available here. 
 
 ```python
 from space2stats import StatsTable

docs/sub_tasks/geest.md

@@ -1,9 +1,9 @@
 # GEEST - Assessing women's employment opportunities
 [For complete project information, please click here](https://worldbank.github.io/GEEST/README.html)
 
-With support from the [Canada Clean Energy and Forest Climate Facility (CCEFCFy)](https://www.worldbank.org/en/topic/climatechange/brief/canada-world-bank-clean-energy-and-forests-climate-facility), the [Geospatial Operational Support Team (GOST, DECSC)](https://worldbank.github.io/GOST) launched the project "Geospatial Assessment of Women Employment and Business Opportunities in the Renewable Energy Sector." The project proposes a novel methodology for mapping the enabling environments for women's employmenty. The goal is to inform new energy projects in client countries to support the advancement of women's economic empowerment while contributing to closing gender gaps in employment in the RE sector.
+With support from the [Canada Clean Energy and Forest Climate Facility (CCEFCF)](https://www.worldbank.org/en/topic/climatechange/brief/canada-world-bank-clean-energy-and-forests-climate-facility), the [Geospatial Operational Support Team (GOST, DECSC)](https://worldbank.github.io/GOST) launched the project "Geospatial Assessment of Women Employment and Business Opportunities in the Renewable Energy Sector." The project proposes a novel methodology for mapping the enabling environments for women's employment. The goal is to inform new energy projects in client countries to support the advancement of women's economic empowerment while contributing to closing gender gaps in employment in the RE sector.
 
-In addition to the methodology, the project has generated an geospatial open-source, QGIS tool for implementing the methodology. For more details, visit the GEEST project directly.
+In addition to the methodology, the project has generated a geospatial open-source, QGIS tool for implementing the methodology. For more details, visit the GEEST project directly.
 
 ```{figure} ../images/WEE_PNG.png
 ---

docs/sub_tasks/geo_enhancement.md

@@ -1,13 +1,13 @@
 # Geospatial Enhancement of Surveys
 [For more project information, please visit project website](https://worldbank.github.io/DECAT_HH_Geovariables/README.html)
 
-This repository contain code and documentation about a collection of activities whose overarching goal is to add geospatial variables to locations from household surveys. For example, given a completed household survey in a country, we can generate anonymized household level coordinates (or enumeration area level coordinates which will be centroids) and link them with variables coming from geospatial data such as precipitation, vegetation indices and more which are otherwise not avaibale in the survey itself. Thus, geoenhancement is a way to enrich survey data with geospatial variables so that analysts can conduct more extended analysis. The repository provides the following:
+This repository contains code and documentation about a collection of activities whose overarching goal is to add geospatial variables to locations from household surveys. For example, given a completed household survey in a country, we can generate anonymized household level coordinates (or enumeration area level coordinates which will be centroids) and link them with variables coming from geospatial data such as precipitation, vegetation indices and more which are otherwise not available in the survey itself. Thus, geoenhancement is a way to enrich survey data with geospatial variables so that analysts can conduct more extended analysis. The repository provides the following:
 
-Survey geo-enhacment process. In-depth information about how the geovariables are generated, rationale for selection of data sources and other design decisions. In addition, we also document best practices for this type of data processing.
+Survey geo-enhancement process. In-depth information about how the geovariables are generated, rationale for selection of data sources and other design decisions. In addition, we also document best practices for this type of data processing.
 
 Data generation for specific surveys. All the required documentation about each survey which has gone through this geo-enhancement is fully covered in this repo. This includes what geovariables were generated, where to find the output geovariables and more.
 
-Spatial anonymization. As you will note from the survey geo-enhancement process, the survey coordinates need to be anonymized first before they are used in the ge-enhancement process and the associated geovariables publicly disseminated. As such, the work covered in this repository included development of tools for robust spatial anonymization. A Python package: [spatial-anoanonymization] (worldbank/Spatial-Anonymization) for this prupose is being developed. In this regard, information about this package and other tools for spatial anonymization and bets practices will also be provided.
+Spatial anonymization. As you will note from the survey geo-enhancement process, the survey coordinates need to be anonymized first before they are used in the geo-enhancement process and the associated geovariables publicly disseminated. As such, the work covered in this repository included development of tools for robust spatial anonymization. A Python package: [spatial-anonymization] (worldbank/Spatial-Anonymization) for this purpose is being developed. In this regard, information about this package and other tools for spatial anonymization and best practices will also be provided.
 
 ```{figure} ../images/geo-enhancement-pipeline.png
 ---