Infrastructure preprocessor

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SeriesRoadRuralAccessIndex is obviously a series we need to look into. The World Bank no longer has this exact index however.

Sources

WDI, ITU, WHO, WSS/JMP

Infrastructure series pulled into preprocessor 

This table was created using DataDict 7.20 and filtering group by: Information Communication, Infrastructure; Information Communication,Infrastructure;   Information Communication, Infrastructure, Trade; Infrastructure, Water, Health; Science technology, infrastructure, Knowledge; and Transportation, Infrastructure.

DataDict 720
Table Group Definition Source Last IFs Update UsedInPreprocessor Years
SeriesRoadRuralAccessIndex Transportation,Infrastructure Rural Access Index, proportion of the rural population who live within 2 km (25 minute walk) of all-weather road The World Bank Rural Access Index 2010/07/15 Yes
SeriesICTExpend%GDP Information Communication,Infrastructure ICT technology expenditure, % of GDP WDI CD 2010 and existing IFs data 2011/05/28 Yes
SeriesWasteWaterColConnect% Infrastructure, Water, Health Percent of population connected to urban wastewater collection system UNSD/ UNEP/ OECD/ EUROSTAT 2011/08/08 Yes
SeriesWasteWaterTreatConnect% Infrastructure, Water, Health Percent of population connected to urban wastewater treatment system UNSD/ UNEP/ OECD/ EUROSTAT 2011/08/08 Yes
SeriesR&D%GDPWDI Science Technology, Infrastructure, Knowledge R&D as % of GDP (in some earlier years was GNI) Constructed from multiple WDI vol;CD incl 01, 02, 04,05,06,07; 2006-2008 Update from UIS Website 2013/08/07 Yes
SeriesICTExport%Exp Information Communication,Infrastructure, Trade Exports of ICT as % of total exports WDI 2013 2013/11/08 Yes
SeriesICTImport%Imp Information Communication,Infrastructure, Trade Imports of ICT as % of total imports WDI 2013 2013/11/08 Yes
SeriesRoadPavedKm Transportation,Infrastructure Length of paved roads, kilometers WDI 2013; WDI 2011; Calderon (communication to DR) and Canning, David (1998) with updates by Canning from WDI 2006 2013/12/17 Yes
SeriesRoadsPaved% Transportation,Infrastructure Roads, paved as a percent of total roads WDI 2014 May BATCH PULL 2014/06/11 Yes
SeriesRoadsTotalNetwork Transportation,Infrastructure Roads, total network, kilometers WDI 2014 May BATCH PULL 2014/06/11 Yes
SeriesVehiclesper1000 Transportation,Infrastructure Vehicles per 1000 People WDI 2014 May BATCH PULL 2014/06/11 Yes
SeriesWHO2012WaterPipedFiltered Infrastructure, Water, Health Proportion of Total population served with Piped Water to premises which is Filtered or Boiled (%) WHO: provided directly by Annette Pruss-Ustun of the WHO 2014/06/24 Yes
SeriesWHO2012WaterPipedNoFilter Infrastructure, Water, Health Proportion of Total population served with Piped Water to premises which is not Filtered or Boiled (%) WHO: provided directly by Annette Pruss-Ustun of the WHO 2014/06/24 Yes
SeriesWHO2012WaterOthImpFiltered Infrastructure, Water, Health Proportion of Total population served with Other Improved Water which is Filtered or Boiled (%) WHO: provided directly by Annette Pruss-Ustun of the WHO 2014/06/25 Yes
SeriesWHO2012WaterOthImpNoFilter Infrastructure, Water, Health Proportion of Total population served with Other Improved Water which is not Filtered or Boiled (%) WHO: provided directly by Annette Pruss-Ustun of the WHO 2014/06/25 Yes
SeriesWHO2012WaterUnImpFiltered Infrastructure, Water, Health Proportion of Total population served with Unimproved Water which is Filtered or Boiled (%). Total Unimproved Water is the sum of Surface and Other Unimproved water access. (This is probably the same as WSSJMP unimproved water) WHO: provided directly by Annette Pruss-Ustun of the WHO 2014/06/25 Yes
SeriesWHO2012WaterUnImpNoFilter Infrastructure, Water, Health Proportion of Total population served with Unimproved Water which is not Filtered or Boiled (%). Total Unimproved Water is the sum of Surface and Other Unimproved water access. (This is probably the same as WSSJMP unimproved water) WHO: provided directly by Annette Pruss-Ustun of the WHO 2014/06/25 Yes
SeriesICTTelephoneLinesPer100 Information Communication, Infrastructure Fixed telephone lines per 100 inhabitants ITU 2014 2014/06/30 Yes
SeriesICTBroadbandSubscribersPer100ITU Information Communication, Infrastructure Fixed broadband subscriptions per 100 inhabitants ITU 2014 BATCH PULL 2015/01/28 Yes
SeriesICTComputerHousehold% Information Communication, Infrastructure Proportion of households with a computer ITU 2014 BATCH PULL 2015/01/28 Yes
SeriesICTTelephoneCellSubscribersPer100 Information Communication, Infrastructure Mobile cellular subscriptions per 100 inhabitants ITU 2014 BATCH PULL 2015/01/28 Yes
SeriesICTBroadbandMobileSubsPer100 Information Communication,Infrastructure Broadband Mobile, Mobile cellular subscriptions with access to data communication at broadband speed per 100 inhabitants ITU 2014 BATCH PULL 2015/02/03 Yes
SeriesICTInternetHousehold% Information Communication, Infrastructure Proportion of households with Internet access at home ITU 2015/04/14 Yes
SeriesICTInternet%Pop Information Communication,Infrastructure Percent of population on-line WDI BATCH PULL 2015/07/14 Yes
SeriesWSSJMPSanitationTotal%Improved Infrastructure, Water, Health Proportion of Total population served with Improved Sanitation (%) WSS JMP WHO/UNICEF JMP 2015/08/25 Yes
SeriesWSSJMPSanitationTotal%OpenDefecation Infrastructure, Water, Health Proportion of Total population served with Open Defecation Sanitation (%) WSS JMP WHO/UNICEF JMP 2015/08/25 Yes
SeriesWSSJMPSanitationTotal%OtherUnimproved Infrastructure, Water, Health Proportion of Total population with Other Unimproved Sanitation (%) WSS JMP WHO/UNICEF JMP 2015/08/25 Yes
SeriesWSSJMPSanitationTotal%Shared Infrastructure, Water, Health Proportion of Total population served with Shared Sanitation (%) WSS JMP WHO/UNICEF JMP 2015/08/25 Yes
SeriesWSSJMPWaterTotal%OtherImproved Infrastructure, Water, Health Proportion of Total population served with Other Improved Water (%) WSS JMP WHO/UNICEF JMP 2015/08/25 Yes
SeriesWSSJMPWaterTotal%OtherUnimproved Infrastructure, Water, Health Proportion of Total population with Other Unimproved Water (%) WSS JMP WHO/UNICEF JMP 2015/08/25 Yes
SeriesWSSJMPWaterTotal%Piped Infrastructure, Water, Health
Proportion of Total population served with Piped Water (%) WSS JMP WHO/UNICEF JMP 2015/08/25 Yes
SeriesWSSJMPWaterTotal%Surface Infrastructure, Water, Health Proportion of Total population with Surface Water (%)
WSS JMP WHO/UNICEF JMP 2015/08/25 Yes

Irrigation

Land equipped for irrigation

  • Area equipped for irrigation (TLANDIRAREAEQUIP) is initialized using SeriesLandIrAreaEquipFAO
    • Source: FAO
    • Definition: Land Area Equipped for Irrigation
    • Note: need to switch to AQUASTAT when it gets updated


  • Potentially irrigable land (CLandIrPotReached) initialized usingSeriesLandIrPotentialReached


  • If CLandIrPotReached is not null, and it is less than the area of land equipped for irrigation (TLANDIRAREAEQUIP)
    • Then set the saturation level (CLandIrAreaSat) as 10% above TLANDIRAREAEQUIP.
  • Otherwise, CLandIrPotReached is the saturation level


  • Potentially irrigable land (CLandIrPot) is initialized using SeriesLandIrPotential
    • Source: AQUASTAT
    • Definition: Irrigation potential (1000 ha)
  • If null then
    • If land equipped for irrigation is null, then
      • Assume potentially irrigable land is 10% of agricultural land area (CLandAgri).
      • Land equipped for irrigation is 33% of potentially irrigable land
      • Saturation level is 29.7% of potentiall irrigable land
        • CLandIRAreaSat(ICount%) = (0.1 + 0.9 * (TLANDIRAREAEQUIP / CLandIrPot)) * CLandIrPot
        • 39.7% because (TLANDIRAREAEQUIP/CLandIrPot) = .33
        • Note: This seems wrong because saturation level (39.7% of potential) is only slightly higher than land equipped (33%)
    • If there is data on land equipped for irrigation then
      • If land equipped for irrigation is greater than 9% of agricultural land and less than 91% of agricultural land then
        • Saturation level (CLandIRAreaSat) is 10% higher than current level of land equipped for irrigation (LANDIRAREAEQUIP)
      • If land equipped for irrigation is less than 9% of agricultural land then
        • Potentially irrigable land is 10% of agricultural land
        • Saturation level is initialized using same equation above: CLandIRAreaSat(ICount%) = (0.1 + 0.9 * (TLANDIRAREAEQUIP / CLandIrPot)) * CLandIrPot
        • Note:This also sets the saturation level low (lower than potentially irrigable, which is 10% of agricultural land)
      • If land equipped for irrigation is greater than 91% of agricultural land then
        • Potentially irrigable land is set as agricultural land
        • Saturation level is again set as: CLandIRAreaSat(ICount%) = (0.1 + 0.9 * (TLANDIRAREAEQUIP / CLandIrPot)) * CLandIrPot
        • Note:There is currently no country where this is the case i.e. this branch of the code is never activated
  • If there is data for potentially irrigable land
    • But there isn't data on land equipped for irrigation
      • Land equipped for irrigation is 33% of potentially irrigable land
    • And there is data on land equipped for irrigation then
      • CLandIRAreaSat(ICount%) = Amin(cLandAgri, AMAX(TLANDIRAREAEQUIP * 1.01, (0.4 + 0.6 * (TLANDIRAREAEQUIP / CLandIrPot)) * CLandIrPot))
      • 'If Potential is less than AreaEuip set the saturation at 1% higher than actual
      • Note:This is not true. If potential is less than equipped, then ((0.4 + 0.6 * (TLANDIRAREAEQUIP / CLandIrPot)) * CLandIrPot) could actually be larger than (LANDIRAREAEQUIP * 1.01)


  • Set the land equipped for irrigation growth rate as
    • CLandIrAreaEquipGR(ICount%) = getAnnualGrowthRate(LandIrAreaEquipTbl, BaseYear, 0.08, -0.08, 3, 5, 15, 10)
    • Note: this calculates a growth rate using historical values. The parameters are (max growth rate, min growth rate, minimum number of years needed to compute growth rate, maximum number of years to compute growth rate, maximum number of years to go backward from BaseYear, maximum number of years to go forward from BaseYear)
  • If unable to calculate (no data) then
    • set as -9999 (which should get adjusted to 0.001)