This dataset contains the following administrative boundaries: ADM0, ADM1, ADM2, ADM3, ADM4, ADM5.

Produced and maintained since 2017, the geoBoundaries Global Database of Political Administrative Boundaries Database www.geoboundaries.org is an open license, standardized resource of boundaries (i.e., state, county) for every country in the world.

The Admin boundary shapefiles for Rwanda since 2006. The shapefile was created in 2006 and updated by the 2012 Census mapping. It was updated again on 17 Oct 2017.

These administrative level 0-3 shapefiles are suitable for database or GIS linkage to the Rwanda administrative level 0-3 population statistics tables.

02 October 2018 update:

Administrative level 1 (province) P-codes corrected at all levels.

27 September 2018 update:

Administrative level 0 (country) feature attributes corrected at all levels.

19 October 2018 update:

to Administrative levels 0 - 3:

Standardized P-codes, based on the pre-existing numeric codes added

Lake Kivu extent removed

Corrections made to two administrative level 1 names in the administrative level 3 (sector) shapefile

21 September 2018 update:

Administrative level 4 P-coded

Rwanda administrative level 0-3 sex and age disaggregated 2023 population statistics projections

REFERENCE YEAR: 2023

These CSV tables are suitable for database or GIS linkage to the Rwanda - Subnational Administrative Boundaries layers using the ADM0. ADM1, ADM2, and ADM3_PCODE fields.

The 2022 data are included for reference.

Rwanda Number of Job Postings: Removed: Administrative and Support and Waste Management and Remediation Services data was reported at 2.000 Unit in 24 Feb 2025. This records an increase from the previous number of 0.000 Unit for 17 Feb 2025. Rwanda Number of Job Postings: Removed: Administrative and Support and Waste Management and Remediation Services data is updated weekly, averaging 0.000 Unit from Jan 2008 (Median) to 24 Feb 2025, with 895 observations. The data reached an all-time high of 18.000 Unit in 18 Oct 2021 and a record low of 0.000 Unit in 17 Feb 2025. Rwanda Number of Job Postings: Removed: Administrative and Support and Waste Management and Remediation Services data remains active status in CEIC and is reported by Revelio Labs, Inc.. The data is categorized under Global Database’s Rwanda – Table RW.RL.JP: Number of Job Postings: Removed: by Industry.

Abstract

The Establishment Census provides information on all economic activities by size of establishments in Rwanda. This information is used to classify establishments according to their size (micro, small, medium, and large) but also into formal and informal establishments. For each establishment census round, the methodology used for data collection and data analysis is quite similar; this helps in carrying out the comparative analysis of the information found in the latest and previous censuses.

The main objectives of this census are: · To provide detailed information on the establishments' characteristics and their spatial distribution; · To provide detailed information about the economic activity of all establishments operating in Rwanda; · To update data of the enterprise database, the general sample frame of economic, administrative and public-service establishments for use in sample surveys. The 2020 Establishment Census is designed to achieve the following specific objectives: · To produce a comprehensive and updated data profile of all economic activities by establishments operating in Rwanda; · To provide detailed tabulations about the establishments' characteristics, e.g, geographical location, number of employees, · Registration status, legal status, ownership, sector of activity, manager or owner sex; · To produce data necessary to classify establishments according to their size (micro, small, medium, and large); · To lay out the data foundation needed to identify formal and informal economic sectors in Rwanda

Geographic coverage

National coverage

Analysis unit

Institutions

Universe

The study covered all establishments except governmental establishments that provide not-for-sale services.However, for employment component, all institutions were covered including 'not-for-sale services' goverment institutions. It is important to note that this census excludes diplomatic misisons operatiing in Rwanda.

Sampling procedure

The methodology used in the Establishment Census 2020 consists of a complete counting of every operating establishment that has a fixed location and that is involved in a specific economic activity. The Establishment Census 2020 covered all 30 districts in Rwanda.

Mode of data collection

Face-to-face [f2f],The content of the questionnaire was broadly similar to that of the Establishment Census 2017. This allows to compare findings of 2020 to the ones of 2017 as well as 2014 and make trends analysis. The instructions manual was developed based on the updated questionnaire. Both the questionnaire and the instructions manual were in English and translated in Kinyarwanda to facilitate the interview. Electronic application was used for data collection during the Establishment Census 2020. Furthermore, data transmission was tested and executed using NISR’s server by electronic devices (android smartphones and tablets

The Rwanda Population-Based Survey (PBS) provides a comprehensive assessment of the current status of agriculture and food security in almost the entire country, including all four provinces and all of rural Rwanda. The Zone of Influence (ZOI) comprises 27 of the 30 districts in Rwanda, with the exception of the three districts of Kigali City. The PBS, which was conducted from December 22, 2012 to January 11, 2013. The overall objective of the survey is to provide baseline on data living standards, nutritional status, and women's empowerment in agriculture in the ZOI.

Rwanda Number of Job Postings: New: Administrative and Support and Waste Management and Remediation Services data was reported at 1.000 Unit in 10 Mar 2025. This records a decrease from the previous number of 3.000 Unit for 03 Mar 2025. Rwanda Number of Job Postings: New: Administrative and Support and Waste Management and Remediation Services data is updated weekly, averaging 0.000 Unit from Jan 2008 (Median) to 10 Mar 2025, with 897 observations. The data reached an all-time high of 15.000 Unit in 05 Feb 2024 and a record low of 0.000 Unit in 27 Jan 2025. Rwanda Number of Job Postings: New: Administrative and Support and Waste Management and Remediation Services data remains active status in CEIC and is reported by Revelio Labs, Inc.. The data is categorized under Global Database’s Rwanda – Table RW.RL.JP: Number of Job Postings: New: by Industry.

Rwanda Number of Job Postings: Active: Administrative and Support and Waste Management and Remediation Services data was reported at 8.000 Unit in 13 Jan 2025. This records an increase from the previous number of 6.000 Unit for 06 Jan 2025. Rwanda Number of Job Postings: Active: Administrative and Support and Waste Management and Remediation Services data is updated weekly, averaging 0.000 Unit from Jan 2008 (Median) to 13 Jan 2025, with 889 observations. The data reached an all-time high of 36.000 Unit in 08 Aug 2022 and a record low of 0.000 Unit in 12 Apr 2021. Rwanda Number of Job Postings: Active: Administrative and Support and Waste Management and Remediation Services data remains active status in CEIC and is reported by Revelio Labs, Inc.. The data is categorized under Global Database’s Rwanda – Table RW.RL.JP: Number of Job Postings: Active: by Industry.

The Soil and Terrain Database for Northeastern Africa contains land resource information on soils, physiography, geology and vegetation for the following ten countries: Burundi, Djibouti, Egypt, Eritrea, Ethiopia, Kenya, Rwanda, Somalia, Sudan and Uganda. The information is accessible with an easy-to-use viewer program and is also stored in vector Arc/Info export format. Information on individual soil properties with class values is also given. A land suitability assessment for irrigated and upland crops for each unit is included. The scale ofthe source material is variable and ranges between 1:1 million and 1:2 million. A user manual for the viewer program and background information on the collected and correlated land resource materials are contained in filed documents.

Soils are classified in the Revised Legend; physiographic and lithology information was collected using an earlier draft version of the SOTER manual.

The Inter-Governmental Authority on Development (IGAD) -- Sudan, Kenya, Djibouti, Somalia, Uganda, Eritrea, Ethiopia -- Crop Production System Zones (CPSZ) software is a detailed database that provides background information about actual farming in the region. It comes with a program (CVIEW, a CPSZ viewer) that displays maps, zooms in and out, and provides export facilities for the maps in image format and for the actual data in text format. The elementary mapping unit is a compromise between administrative units and agro-ecological zones: whenever steep agro-ecological gradients exist, administrative units are subdivided, thus resulting in 1200 mapping units that are homogeneous from an agro-ecological point of view, while retaining the compatibility with the administrative units used for most socio-economic variables in agricultural planning.

The just over 500 mappable variables are subdivided into several categories covering the spectrum from agronomy and livestock to the physical environment. For each mapping unit, detailed information is also presented on the crop calendar, typical yields and main pests and diseases.

This CD-ROM contains a collection of land and natural resource information for Northeastern Africa, in particular for the IGAD countries bordering the Nile basin. It includes data on administrative boundaries, rivers and lakes, soil and terrain, climatology, land use, physiography, geology and natural vegetation in easily accessible format.

Soil and Terrain Database for Northeasterm Africa (1:1 Million Scale) and Crop Production System Zones of the IGAD Subregion is provided on CD-ROM by the FAO, Land and Water Digital Media Series (Number 2). The CD-ROM can be purchased (Price: US$40) from FAO, Sales and Marketing Group, Viale delle Terme di Caracalla 0100 Rome, Italy (Fax: +39-06-5705-3360 E-mail: publications-sales@fao.org).

This dataset contains administrative polygons grouped by country (admin-0) with the following subdivisions according to Who's On First placetypes:
- macroregion (admin-1 including region)
- region (admin-2 including state, province, department, governorate)
- macrocounty (admin-3 including arrondissement)
- county (admin-4 including prefecture, sub-prefecture, regency, canton, commune)
- localadmin (admin-5 including municipality, local government area, unitary authority, commune, suburb)

The dataset also contains human settlement points and polygons for:
- localities (city, town, and village)
- neighbourhoods (borough, macrohood, neighbourhood, microhood)

The dataset covers activities carried out by Who's On First (WOF) since 2015. Global administrative boundaries and human settlements are aggregated and standardized from hundreds of sources and available with an open CC-BY license. Who's On First data is updated on an as-need basis for individual places with annual sprints focused on improving specific countries or placetypes. Please refer to the README.md file for complete data source metadata. Refer to our blog post for explanation of field names.

Data corrections can be proposed using Write Field, an web app for making quick data edits. You’ll need a Github.com account to login and propose edits, which are then reviewed by the Who's On First community using the Github pull request process. Approved changes are available for download within 24-hours. Please contact WOF admin about bulk edits.

Rwanda administrative division with aggregated population. Built from Kontur Population: Global Population Density for 400m H3 Hexagons on top of OpenStreetMap administrative boundaries data. Enriched with HASC codes for regions taken from Wikidata.
Global version of boundaries dataset: Kontur Boundaries: Global administrative division with aggregated population

Cell boundary data contains cell boundaries for Rwanda since 2006. The data was created in 2006 and updated by the 2012 Census mapping. The census mapping started in 2011 and ended in 2012, where a team of 80 field staff collected census and administrative boundaries up to the lowest administrative level which is "Umudugudu". Boundaries were adjusted in the GIS lab using the 2008-2009 aerial photographs (Orthophoto) taken by the Rwanda Natural Resource Authority. The datasets was updated by the 2022 Population and Housing census.Cell is one of the administrative entities in Rwandan administration, they are under Sector authority. The total number of cells is two thousands one hundred forty eight (2148). The data contain the following attributes: Prov_ID contains one digit as unique identifier for each province, Province contains Province names in English, Dist_ID contains two digits as unique identifier for each District, a combination of the first digit for Province and the second one for District. District contains District names, Sect_ID: Contains a four digit number as unique identifier for each sector, Sector contains Sector names, Cell_ID contains a six digit number as unique identifier for each cell. This Unique Id can be used to link the data to other thematic tables. The attribute Cell contains Cell names. The data format is in decimal degrees using the World Geodetic System (WGS) 1984. The geographic coverage of the dataset excludes some are of the country covered by the Kivu Lake.

Abstract

This is a panel dataset of monthly price data from a selected sample of rural markets in Rwanda, with data points starting in January 2017 and ending in November 2020.

Geographic coverage

The dataset includes markets in 21 districts of Rwanda, covering all 4 provinces.

Analysis unit

Product (one observation per product per month per market).

Kind of data

Sample survey data [ssd]

Sampling procedure

Markets were purposively sampled, based on proximity to roads prioritized for rehabilitation by the Government of Rwanda.

Mode of data collection

Computer Assisted Personal Interview [capi]

Cleaning operations

2-3 price points were taken per product at each market per month. In order to deal with outliers, prices were logged and averaged to get mean monthly prices, then exponentiated to be interpretable. Where a product was not available, there is no price data. However, there are cases where a product was available but price data was not available.

UNOSAT code FL20230504RWA, GDACS Id: 1101977 This map illustrates satellite-detected surface waters in Kigali City, Southern, Northern, Western, and Eastern Provinces, Rwanda as observed from a Sentinel-1 image acquired on 3 May 2023 at 18:20 local time. Within the analyzed area of about 6,000 km2, about 20 km2 of land appear to be flooded. Based on Worldpop population data and the detected surface waters in the analyzed area, the potentially exposed population is mainly located in Southern Province Zambezia province with ~7,000 people, and Kigali City with ~5,600 people.

This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

Abstract

The EICV5 survey (Enquête Intégrale sur les Conditions de Vie des ménages) was conducted over a 12-month cycle from October 2016 to October 2017. Data collection was divided into 10 cycles in order to represent seasonality in the income and consumption data. A main cross-sectional sample survey, a panel survey and a VUP sample survey were conducted simultaneously.

The objectives of the EICV5 Panel Survey are to measure the trends in key socioeconomic indicators over time for a nationally representative panel of households. EICV5 aims to provide timely and updated statistics to facilitate monitoring progress on poverty reduction programmes and evaluation of different policies as stipulated in the First National Strategy for Transformation (NST1), the 2030 Sustainable Development Goals (SDGs), as well as the Vision 2020 and Vision 2050. The survey data are also very important for national accounts and updating the consumer price index (CPI).

Geographic coverage

National coverage.

Analysis unit

Households

Universe

All household members

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the EICV5 cross-sectional survey is based on the NISR master sample data. More recently, the NISR used the 2012 Census frame to select a large master sample of villages 3,960 that can be used for the different national household surveys in Rwanda. The primary sampling units (PSUs) for the Master Sample are individual villages, or a combination of small villages, with the number of households tabulated from the 2012 Census data. A new listing of households was conducted in order to update the frame for the EICV5 cross-sectional survey. The sample households in the EICV5 sample villages were selected from the new listing.

1) The EICV5 Cross-sectional survey sample size

The sample size for the EICV5 cross-sectional survey depends on the level of precision that is required for key indicators at the district level, as well as on resource constraints and logistical considerations. It is very important to ensure good quality control in order to minimize the non-sampling errors. The estimates of the sampling errors for the poverty rate by district from the EICV4 data were examined in order to determine whether it would be necessary to adjust the sample size. For EIVC4 the number of households selected per cluster was 9 for Kigali Province, which is mostly urban, and 12 for the remaining provinces, which are mostly rural. This sampling strategy has been consistent for all the EICV surveys because it is statistically efficient and is also effective for the EICV logistics of the fieldwork and the workload of the team of enumerators each cycle. The urban areas generally have a higher intraclass correlation for socioeconomic characteristics between households within a cluster compared to rural areas. There is also a different interviewing schedule for the sample households in Kigali Province, so only 9 households are interviewed in each cluster. In terms of the number of sample clusters allocated to each district, it should be a multiple of 10 so that the sample can be evenly distributed to the 10 cycles. In the case of EICV4 the districts in Kigali Province were assigned 5 sample clusters each month, and in the other provinces each district was assigned 4 sample clusters each month.

In EICV5, the sample was increased for the districts in Kigali Province because the estimates of the poverty rate for those districts had higher coefficients of variation (CVs) or relative standard errors (RSEs) compared to the other districts. However, one reason why the RSEs for the districts of Kigali Province were higher is that the value of the poverty rate is lower for these districts. It was pointed out that in the case of estimates of percentages or proportions, it is more effective to use the margin of error to study the sample size. The margin of error is equal to half of the width of the 95% confidence interval, or 1.96 times the standard error. Therefore, the margins of error for the estimates of the poverty rate by district were also examined. In this case the margins of error were also higher for the districts of Kigali Province, given the relatively higher design effects (especially for Gasabo District), and considering that the number of sample households for these districts in EICV4 was only 450, compared to 480 sample households in the districts of the other provinces. For these reasons, it was decided to increase the number of sample PSUs for each district in Kigali Province from 50 to 60, for a total increase of 30 sample clusters and 270 sample households. For the districts in the other provinces it was decided to have the same sample size of 40 clusters and 480 households each cycle, since the level of precision of the EICV4 results for these districts was considered satisfactory.

The sample PSUs in each district were allocated to the urban and rural strata proportionately to the number of households in the 2012 Census frame. In the case of districts where the proportional number of sample PSUs was only 1 for the urban stratum, the number of sample PSUs was increased to 2. For the selection of sample villages for EICV5, it was assumed that the Master Sample villages for each district were explicitly stratified by urban and rural areas. A separate subsample of villages was selected within each stratum from the Master Sample.

At the national level, there are 1,260 sample villages and 14,580 sample households. In the urban strata there are 245 sample villages and 2,526 sample households, and in the rural strata there are 1,015 sample villages and 12,054 sample households. The sample size for the EICV5 cross-sectional survey has 30 more sample PSUs and 270 more sample households than the corresponding sample for EICV4.

In the case of EICV4 the national sample of 177 villages selected from EICV3 for the Panel Survey were also used as part of the EICV4 cross-sectional survey. However, for EICV5 it was decided to select a completely separate sample of villages for the cross-sectional survey.

2) Assignment of sample villages to cycles and sub-cycles

Similar to the EICV4 methodology, a nationally-representative sample of clusters will be assigned for the EICV5 data collection each cycle, so that the sample is geographically representative over time. A subsample serial number from 1 to 10 can be assigned systematically to the geographically ordered list of all sample clusters in each district. In order to assign the cycles to the EICV5 cross-sectional sample villages, random cycle numbers from 1 to 10 were generated to identify the selection sequence. For the 27 districts outside of Kigali Province, the sub-cycle numbers of 1 or 2 were assigned systematically with a random start. This process ensured that the final distribution of the sample clusters to cycles and sub-cycles was geographically representative within each district.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

The same questionnaire was used for cross-sectional, panel and VUP samples. Part A of the questionnaire contains modules on household and individual information. Part B is on agriculture and consumption. The questionnaire was developed in English, and translated into Kinyarwanda.

Questionnaire design took into account the requests raised by major data users and stakeholders, as well as consistency with the previous EICV questionnaires. In addition to methodological improvements, some simplifications were made:

-The major changes introduced in this survey were changes to Section 6, the Economic Activity. Further questioning was added on unemployment and underemployment in response to questions from users, and also to comply with international standards. The section was simplified to enable the analysis to be undertaken by local analysts.

-The Section on the VUP participation was expanded to provide more information, better classification of beneficiaries and to provide greater consistency within the questionnaire. The same questionnaire is to be used on the separate VUP sample which runs in parallel with the EICV5

Questionnaire was tested in pilot surveys and amended in time prior to the fieldwork starting in October 2016. The complete questionnaire is provided as external resources.

Cleaning operations

A day before the interview started, the enumerator, accompanied by a controller, did an introduction to household, explaining how often they will come in that household and delivering a letter indicating that the HH has been selected.

During the field work, after each cycle, the data processing team produced tables and reports of inconsistencies, which were checked by the field supervisor. The data entry system also contained consistency checks that alerted the data entry operators. In case of an alert, the questionnaire was sent back to the supervisor of data entry for correction.

Response rate

The response rate for EICV5 (cross-sectional) is 100%. All households sampled(14,580) were interviewed with no refusal.

Zambia Province Boundaries provides a 2023 boundary with a total population count. The layer is designed to be used for mapping and analysis. It can be enriched with additional attributes using data enrichment tools in ArcGIS Online.The 2023 boundaries are provided by Michael Bauer Research GmbH. These were published in October 2023. A new layer will be published in 12-18 months. Other administrative boundaries for this country are also available: Country District

UNOSAT code FL20230504RWA, GDACS Id: 1101977 This map illustrates satellite-based damage assessment along the Sebaya River, Rubavu and Karongi Districts, Western Province, Rwanda detected by using a Pleiades very high resolution satellite image acquired on 6 May 2023. Within the analyzed area, UNOSAT identified 355 affected and potentially affected structures.

This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).

Abstract

The EICV-W4 survey (Enquête Intégrale sur les Conditions de Vie des ménages) was conducted over a 12-month cycle from October 2013 to October 2014. Data collection was divided into 10 cycles in order to represent seasonality in the income and consumption data. A main cross-sectional sample survey, a panel survey and a VUP sample survey were conducted simultaneously.

The EICV-W4 provides information on poverty and living conditions in Rwanda and measures changes over time as part of the on-going monitoring of the Poverty Reduction Strategy and other Government policies. The survey data are also very important for national accounts and updating the consumer price index (CPI).

Geographic coverage

National coverage, including rural and urban households and allowing province- and district-level estimation of key indicators

Analysis unit

Households

Universe

All household members (variable s1q15 identifies household membership).

Kind of data

Sample survey data [ssd]

Sampling procedure

The EICV4 cross sectional (CS) sample includes two independent subsets selected using different sampling frames: 1) a new EICV4 sample of households in enumeration areas (EAs) selected using the 2012 Rwanda Population and Housing Census frame and 2) a panel of households selected from 177 EICV3 villages. A new listing of households was conducted in both the panel and new sample clusters in order to update the frame for the CS Survey. The sample households in the new CS sample EAs were selected from the new listing.

1) The new EICV4 sample The main sampling frame for the EICV4 is based on the 2012 Rwanda Census. The primary sampling units (PSUs) are the 2012 census Enumeration Areas (EAs). In the Census, each EA was classified as urban, semi-urban, peri-urban or rural. The urban areas include Kigali-Ville and the district capitals. The semi-urban areas generally correspond to smaller towns that have service facilities and markets. The peri-urban areas currently have the characteristics of rural areas, but they are located on the periphery of urban areas and are designated for future development. For the EICV4 sampling frame, the semi-urban areas were grouped with the urban strata, and the peri-urban areas with the rural strata. This results in a final distribution of 17.2% urban households and 82.8% rural households in the sampling frame. EAs in the 177 EICV3 sample villages selected for the panel study were excluded from the sampling frame, in order to avoid any overlap between the two samples.

The new EICV4 sample of 12,312 households was selected using a stratified two-stage design. At the first stage, sample EAs were selected within each stratum (district) with probability proportional to size (PPS) from the ordered list of EAs in the sampling frame. The EAs are implicitly stratified by urban and rural strata within each district, ordered first by urban, semi-urban, peri-urban and rural areas, and then geographically by sector, cellule, village and EA codes. This first stage sampling procedure provides a proportional allocation of the sample to the urban and rural areas of each district. At the second stage, households in each sample EA are selected from the listing. For the three districts in Kigali Province, 9 households were selected in each sample EA as original households; for the remaining 27 districts, 12 households were selected in each sample EA as original households. In addition, a reserve sample of 3 replacement households were selected for each sample EA in Kigali Province and 4 replacement households for each sample EA in the remaining provinces.

This new EICV4 sample contains 12,312 households, including 12,233 original households and 79 replacement households.

2) Households from 177 EICV3 villages used for panel study The second component of the EICV4 cross sectional sample consists of all the sample households interviewed inside the 177 EICV3 villages selected for the panel study (including any replacements households and panel split households inside the clusters).

Within each of the 177 villages, all households that were interviewed during EICV3 were included in the cross-sectional sample. When an EICV3 sample household moved and a new household occupied the same house in the cluster, it was interviewed for the Cross-Sectional Survey, and assigned a PID (dependency) code of 94. If an EICV3 household was empty or not found, a random replacement household was selected for the EICV4 Cross-Sectional Survey from the new listing of the sample cluster, and assigned a PID code of 95. The sample households with PID codes 94 and 95 are only used for the cross-sectional study, not the panel study.

This second component of the cross-sectional sample includes 2108 households drawn from the 177 EICV3 villages sampled for the panel study. These include 1604 original EICV3 households, 181 dependent household splitting from the original household in the same cluster, along with 243 households living in the dwelling formerly occupied by a panel household and 80 replacement households in the cluster in order to have 9/12 households per cluster.

The reason why we combine the EICV4 data from the new and panel clusters for the CS analysis is to obtain the most accurate CS estimates. In the case of the CS estimates from the combined samples, the additional data from the 177 sample panel clusters will result in a significant reduction in the variance component of the MSE. Although the bias of the CS data from the sample panel clusters may slightly increase the bias component, this bias is very small compared to the corresponding reduction in the variance component. Therefore the CS results from the EICV4 data for the combined new and panel clusters can be considered more accurate than the corresponding results using only the EICV4 data for the new sample clusters.

In total, the final EICV4 cross-sectional sample contains 14,419 households.

3) Assignment of EAs to cycles and sub-cycles Data collection covering a period of 12 month is divided into 10 cycles to represent seasonality in consumption, income, employment and agricultural activity patterns. For rural enumeration, each cycle is further divided into two sub-cycles. For the 177 EICV3 villages, the cycle and sub-cycle were pre-determined. Households were re-interviewed in the same cycle, corresponding to the same time of the year as they were in EICV3. To assign cycles to the new EICV4 sample EAs, random cycle numbers from 1 to 10 were generated to identify the selection sequence. For the 27 districts outside Kigali, sub-cycle numbers of 1 or 2 were assigned systematically with a random start. This process ensured that the final distribution of the sample EAs to cycles and sub-cycles was geographically representative within each district.

Mode of data collection

Face-to-face paper [f2f]

Research instrument

The same questionnaire was used for cross-sectional, panel and VUP samples. Part A of the questionnaire contains modules on household and individual information. Part B is on agriculture and consumption. The questionnaire was developed in English, and translated into Kinyarwanda.

Questionnaire design took into account the requests raised by major data users and stakeholders, as well as consistency with the previous EICV questionnaires. In addition to methodological improvements, some simplifications were made:

-The major changes introduced in this survey were changes to Section 6, the Economic Activity. Further questioning was added on unemployment and underemployment in response to questions from users, and also to comply with international standards. The section was simplified to enable the analysis to be undertaken by local analysts.

-The Section on the VUP participation was expanded to provide more information, better classification of beneficiaries and to provide greater consistency within the questionnaire. The same questionnaire is to be used on the separate VUP sample which runs in parallel with the EICV4

-The health section was reduced to try to cut respondent burden, as health-related information is being collected by Demographic and Health Surveys (DHS).

-The expenditure section was changed in minor ways to provide better information for national accounts (housing investment) and for CPI weights (retail outlets).

Questionnaire was tested in pilot surveys and amended in time prior to the fieldwork starting in October 2013.

The complete questionnaire is provided as external resources.

Cleaning operations

A day before the interview started, the enumerator, accompanied by a controller, did an introduction to household, explaining how often they will come in that household and delivering a letter indicating that the HH has been selected.

During the field work, after each cycle, the data processing team produced tables and reports of inconsistencies, which were checked by the field supervisor. The data entry system also contained consistency checks that alerted the data entry operators. In case of an alert, the questionnaire was sent back to the supervisor of data entry for correction.

Response rate

Out of the 12,312 sample households selected in the new sample clusters for EICV4, only 79 were non-interviews, for a response rate of 99.4% for this sample. All of the 79 non-interviews were replaced. There were only 12 refusals, and there were few cases of houses that were empty or not found, given that the listing was conducted very close to the interviewing period.

UNOSAT code FL20230504RWA, GDACS Id: 1101977 This map illustrates satellite-detected surface waters in Kigali City, Southern, Northern, Western, and Eastern Provinces, Rwanda as observed from a Sentinel-1 image acquired on 5 May 2023 at 05:45 local time. Within the analyzed area of about 6,000 km², about 54 km² of land appear to be flooded. Water extent appears to have increased by about 34km² since 3 May 2023. Based on Worldpop population data and the detected surface waters in the analyzed area, about 34,600 people are potentially exposed or living close flooded areas mainly along the Nyabarongo river, the potentially exposed population is mainly located in Southern Province with ~11,000 people, and Eastern Province with ~6,000 people
This is a preliminary analysis and has not yet been validated in the field. Please send ground feedback to United Nations Satellite Centre (UNOSAT).
Important note: Flood analysis from radar images may underestimate the presence of standing waters in built-up areas and densely vegetated areas due to the backscattering properties of the radar signal.

Mineral resource occurrence data covering the world, most thoroughly within the U.S. This database contains the records previously provided in the Mineral Resource Data System (MRDS) of USGS and the Mineral Availability System/Mineral Industry Locator System (MAS/MILS) originated in the U.S. Bureau of Mines, which is now part of USGS. The MRDS is a large and complex relational database developed over several decades by hundreds of researchers and reporters. While database records describe mineral resources worldwide, the compilation of information was intended to cover the United States completely, and its coverage of resources in other countries is incomplete. The content of MRDS records was drawn from reports previously published or made available to USGS researchers. Some of those original source materials are no longer available. The information contained in MRDS was intended to reflect the reports used as sources and is current only as of the date of those source reports. Consequently MRDS does not reflect up-to-date changes to the operating status of mines, ownership, land status, production figures and estimates of reserves and resources, or the nature, size, and extent of workings. Information on the geological characteristics of the mineral resource are likely to remain correct, but aspects involving human activity are likely to be out of date.