| iso3 | source | type | year | var_short | var_long | residence | san_service_chain | value |
|---|---|---|---|---|---|---|---|---|
| AFG | MICS | Survey | 2003 | s_imp_u | Improved | urban | user interface | 44.2 |
| AFG | NRVS | Survey with microdata | 2005 | s_imp_u | Improved | urban | user interface | 62.3 |
| AFG | NVRA | Survey with microdata | 2008 | s_imp_u | Improved | urban | user interface | 58.3 |
| AFG | MICS | Survey with microdata | 2011 | s_imp_u | Improved | urban | user interface | 70.9 |
openjmp
the data and code the behind the JMP WASH estimates
Linda Karani
Lars Schöbitz
March 10, 2023
openjmp - why?
WHO/UNICEF Joint Monitoring Programme (JMP)
- JMP mandate: internationally-comparable information on WASH since 1990
- JMP data input: raw database is updated every 2 years
- JMP methods: linear regression model with Stata 14.0
- JMP data output: 26 indicators for 232 countries, areas, and globally
- JMP country files: compile raw data input and data output for 26 indicators in spreadhsheet-based proprietary software
Current JMP workflow
WHO/UNICEF Joint Programme for Water Supply, Sanitsation and Hygiene (JMP) (2018)
Goals of openjmp project
- Document and publish R data package:
jmpinput - Document and publish R software package:
jmpmodel - Host half day online workshops to teach usage of developed packages in R
- Publish lessons as Open Educational Resources
jmpinput
jmpinput R data package - benefits
- Data accessible as a single table for any data analysis tool
- Data can be imported to R using one command
- Public website with detailed documentation _ e.g.
washdataR Package https://katilingban.io/washdata/index.html
jmpinput R data package - benefits
- Data accessible as a single table for any data analysis tool
- Data can be imported to R using one command
- Public website with detailed documentation _ e.g.
washdataR Package https://katilingban.io/washdata/index.html
“Urban Water and Sanitation Survey Dataset” (n.d.)
jmpinput - sanitation
- Data in long format (19,528 rows)
- 9 variables
jmpinput - new variables
residence: urban/rural/nationalsan_service_chain: sanitation service chain
| san_service_chain | n |
|---|---|
| open defecation | 2770 |
| sharing | 1553 |
| user interface | 12638 |
| containment | 195 |
| emptying | 1356 |
| transport | 10 |
| FS treatment | 85 |
| WW treatment | 921 |
jmpinput - use cases
- Using JMP methods to reproduce estimates and apply different models - Linda Karani - MSc Data Science
- Writing the
jmpmodelR software package with a function to produce estimates (and a function to produce service ladder plots)
jmpinput - use cases
- Using JMP methods to reproduce estimates and apply different models - Linda Karani - MSc Data Science
- Writing the
jmpmodelR software package with a function to produce estimates (and a function to produce service ladder plots)
jmpinput - use cases
- Using JMP methods to reproduce estimates and apply different models - Linda Karani - MSc Data Science
- Writing the
jmpmodelR software package with a function to produce estimates (and a function to produce service ladder plots)
- Great potential for unforeseen use cases enabled by making the data readily accessible (research, teaching, joining with other data, etc.)
| type | n |
|---|---|
| Survey with microdata | 11149 |
| Admin | 3369 |
| Survey | 3124 |
| Census | 1732 |
| Other | 154 |
| country | n |
|---|---|
| Peru | 412 |
| Mexico | 392 |
| Colombia | 364 |
| Nigeria | 332 |
| Brazil | 304 |
| Costa Rica | 302 |
| South Africa | 301 |
| Japan | 278 |
| Uganda | 265 |
| Bolivia | 256 |
| country | n |
|---|---|
| Peru | 101 |
| Colombia | 96 |
| Nigeria | 80 |
| Mexico | 79 |
| Ghana | 62 |
| Uganda | 62 |
| Costa Rica | 56 |
| Guatemala | 56 |
| South Africa | 52 |
| Bolivia | 48 |
| country | n |
|---|---|
| Philippines | 62 |
| Nigeria | 48 |
| Bangladesh | 40 |
| Japan | 40 |
| South Korea | 32 |
| Ethiopia | 20 |
| Niger | 20 |
| Belarus | 16 |
| China | 16 |
| Congo - Kinshasa | 16 |
| country | n |
|---|---|
| Chile | 14 |
| Hong Kong SAR China | 10 |
| South Korea | 8 |
| Macao SAR China | 8 |
| Mauritius | 8 |
| Norway | 8 |
| Hungary | 6 |
| Armenia | 5 |
| Belgium | 5 |
| Brazil | 5 |
| country | n |
|---|---|
| South Korea | 8 |
| Lithuania | 5 |
| Norway | 5 |
| Japan | 3 |
| Poland | 3 |
| Bhutan | 2 |
| Austria | 1 |
| Finland | 1 |
| Iceland | 1 |
openjmp - what’s next?
openjmp - what’s next
- start of project: ~ July/August 2023
- timeline and activities for
jmpinput&jmpmodel: Work Packages & Activities Google Sheet - stay up to date on progress and add requests: openwashdata/jmpinput/issues
- join openwasdata community: openwashdata.org
Thanks! 🌻
Thanks 🌻
This project was supported by the Open Research Data Program of the ETH Board.
The slides were created via revealjs and Quarto: https://quarto.org/docs/presentations/revealjs/
You can view source code of slides on GitHub
Or you can download slides in PDF format
This material is licensed under Creative Commons Attribution Share Alike 4.0 International.
References
“Urban Water and Sanitation Survey Dataset.” n.d. https://katilingban.io/washdata/index.html. Accessed March 9, 2023.
WHO/UNICEF Joint Programme for Water Supply, Sanitsation and Hygiene (JMP). 2018. “JMP Methodology - 2017 Update & SDG Baselines,” March. https://doi.org/https://washdata.org/report/jmp-methodology-2017-update.
openjmp the data and code the behind the JMP WASH estimates Linda Karani karanilinda@gmail.com Lars Schöbitz lschoebitz@ethz.ch March 10, 2023