These data were compiled to help understand how climate change may impact dryland pinyon-juniper ecosystems in coming decades, and how resource management might be able to minimize those impacts. Objective(s) of our study were to model the demographic rates of PJ woodlands to estimate the areas that may decline in the future vs. those that will be stable. We quantified populations growth rates across broad geographic areas, and identified the relative roles of recruitment and mortality in driving potential future changes in population viability in 5 tree species that are major components of these dry forests. We used this demographic model to project pinyon-juniper population stability under future climate conditions, assess how robust these projected changes are, and to identify where on the landscape management strategies that decrease tree competition would effectively resist population decline. These data represent estimated recruitment, mortality and population growth across the distribution of five common pinyon-juniper species across the US Southwest. These data were collected by the US Forest service in their monitoring program, which is a systematic survey of forested regions across the entire US. Our data is from western US states, including AZ, CA, CO, ID, MT, NM, ND, NV, OR, SD, TX, UT, and was collected between 2000-2007, depending on state census collection times. These data were collected by the Forest Inventory and Analysis program of the USDA US Forest Service. Within each established plot, all adult trees greater than 12.7 cm (5 in.) diameter at breast height (DBH) are assigned unique tags and tracked within four, 7.32 m (24 ft.) radius subplots. All saplings <12.7 cm & > 2.54 cm (1 in.) DBH are assigned unique tags and tracked within four, 2.07 m (6.8 ft.) radius microplots within the larger adult plots. Finally, seedlings <2.54 cm DBH are counted within the same microplots as the saplings. Two censuses were conducted 10 years apart in each plot. These data can be used to inform how tree species have unique responses to changing climate conditions and how management actions, like tree density reduction, may effectively resist transformation away from pinyon-juniper woodland to other ecosystem types.

Abstract

The Sahel Women Empowerment and Demographic Dividend (P150080) project is a regional project aiming to accelerate the demographic transition by addressing both supply- and demand-side constraints to family planning and reproductive and sexual health. To achieve its objective, the project targets adolescent girls and young women mainly between the ages of 8 and 24, who are vulnerable to early marriage, teenage pregnancy, and early school drop-out. The project targeted 9 countries of the Sahel and Western Africa (Benin, Burkina Faso, Cameroon, Chad, Côte d’Ivoire, Guinea, Mali, Mauritania, and Niger) and is expanding in other African countries. The SWEDD is structured into three main components: component 1 seeks to generate demand for reproductive, maternal, neonatal, child health and nutrition products and services; component 2 seeks to improve supply of these products and qualified personnel; and component 3 seeks to strengthen national capacity and policy dialogue.

The World Bank Africa Gender Innovation Lab and its partners are conducting rigorous impact evaluations of key interventions under component 1 to assess their effects on child marriage, fertility, and adolescent girls and young women’s empowerment. The interventions were a set of activities targeting adolescent girls and their communities, designed in collaboration with the government of Côte d’Ivoire. These were (i) safe spaces to empower girls through the provision of life skills and SRH education; (ii) support to income-generating activities (IGA) with the provision of grants and entrepreneurship training; (iii) husbands’ and future husbands’ clubs, providing boys of the community with life skills and SRH education; and finally (iv) community sensitization by religious and village leaders. The latter two have the objective to change restrictive social norms and create an enabling environment for girls’ empowerment.

These data represent the first round of data collection (baseline) for the impact evaluation.

Geographic coverage

Mali, Regions of Kayes, Ségou and Sikasso

Analysis unit

Households, individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The baseline sample comprises 8776 households and 7463 girls living in the regions of Kayes, Sikasso and Ségou in Mali. To define the sample, we partnered with INSTAT Mali. At first, INSTAT conducted a census of the population living in the areas around the 49 schools selected by the education focal point that will all benefit from the SWEDD program. Therefore, census activities were concentrated in 287 villages located within a radius of 10/15km around these schools. Eventually, 10 villages had to be dropped due to security reasons. Keeping with the eligibility criteria of surveying villages where there were at least 10 households with a girl aged between 12 and 24 years old, 270 villages were eventually sampled. Households were surveyed before randomization into groups assigned to receive the SWEDD program.

The objective of the baseline survey was to build a comprehensive dataset, which would serve as a reference point for the entire sample, before treatment and control assignment and program implementation.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

The questionnaire administrated to girls contains the following sections: 1. Education 2. Marriage and children 3. Aspirations 4. Health and family planning 5. Knowledge of HIV/AIDS 6. Women's empowerment 7. Gender-based violence 8. Income-generating activities 9. Savings and credit 10. Personal relationships and social networks 11. Committee members and community participation

The household questionnaire was administered to the head of the household or to an authorized person capable of answering questions about all individuals in the household. The adolescent questionnaire was administered to an eligible pre-selected girl within the household. Considering the modules of the adolescent questionnaire, it was only administered by female enumerators. The questionnaires were written in French, translated into Bambara, and programmed on tablets in French using the CAPI program.

Census Information By Radio
This dataset falls under the category Traffic Generating Parameters Population.
It contains the following data: Census information of the City, disaggregated by radius.
This dataset was scouted on 2022-02-20 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.buenosaires.gob.ar/dataset/informacion-censal-por-radio

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in France, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods:

For more information please visit:

· Python Package: https://github.com/datasciencecampus/transport-network-performance

· Docker Image: https://github.com/datasciencecampus/transport-performance-docker

Known Limitations/Caveats:

These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below.

Urban Centre and Population Estimates:

· Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details.

Public Transit Schedule Data (GTFS):

· Does not include effects due to delays (such as congestion and diversions).

· Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning.

Transport Network Routing:

· “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary.

Please also visit the Python package and Docker Image GitHub issues pages for more details.

How to Contribute:

We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

Characteristics of included infants from the BILD cohort.

Polygons in this layer represent low food access areas: areas of the District of Columbia which are estimated to be more than a 10-minute walk from the nearest full-service grocery store. These have been merged with Census poverty data to estimate how much of the population within these areas is food insecure (below 185% of the federal poverty line in addition to living in a low food access area).Office of Planning GIS followed several steps to create this layer, including: transit analysis, to eliminate areas of the District within a 10-minute walk of a grocery store; non-residential analysis, to eliminate areas of the District which do not contain residents and cannot classify as low food access areas (such as parks and the National Mall); and Census tract division, to estimate population and poverty rates within the newly created polygon boundaries.Fields contained in this layer include:Intermediary calculation fields for the aforementioned analysis, and:PartPop2: The total population estimated to live within the low food access area polygon (derived from Census tract population, assuming even distribution across the polygon after removing non-residential areas, followed by the removal of population living within a grocery store radius.)PrtOver185: The portion of PartPop2 which is estimated to have household income above 185% of the federal poverty line (the food secure population)PrtUnd185: The portion of PartPop2 which is estimated to have household income below 185% of the federal poverty line (the food insecure population)PercentUnd185: A calculated field showing PrtUnd185 as a percent of PartPop2. This is the percent of the population in the polygon which is food insecure (both living in a low food access area and below 185% of the federal poverty line).Note that the polygon representing Joint Base Anacostia-Bolling was removed from this analysis. While technically classifying as a low food access area based on the OP Grocery Stores layer (since the JBAB Commissary, which only serves military members, is not included in that layer), it is recognized that those who do live on the base have access to the commissary for grocery needs.Last updated November 2017.

Risk factors for respiratory symptoms.

Geographic proximity to service centres and population centres is an important determinant of socio-economic and health outcomes. Consequently, it is a relevant dimension in the analysis and delivery of policies and programs. To measure this dimension, Statistics Canada developed an Index of Remoteness of communities. For each populated community (census subdivision), the index is determined by its distance to all the population centres defined by Statistics Canada in a given travel radius, as well as their population size.

Experimental public transit transport performance statistics by 200 metre grids for a subset of urban centres in Great Britain, with the following fields (Note: These data are experimental, please see the Methods and Known Limitations/Caveats Sections for more details).AttributeDescriptionidUnique IdentifierpopulationGlobal Human Settlement Layer population estimate downsampled to 200 metre (represents the total population across adjacent 100 metre cells)access_popThe total population that can reach the destination cell within 45 minutes using the public transit network (origins within 11.25 kilometres of the destination cell)proxim_popThe total population within an 11.25 kilometre radius of the destination celltrans_perfThe transport performance of the 200 metre cell. The percentage ratio of accessible to proximal populationcity_nmName of the urban centrecountry_nmName of the country that the urban centre belongs toMethods:

For more information please visit:

· Python Package: https://github.com/datasciencecampus/transport-network-performance

· Docker Image: https://github.com/datasciencecampus/transport-performance-docker

Known Limitations/Caveats:

These data are experimental – see the ONS guidance on experimental statistics for more details. They are being published at this early stage to involve potential users and stakeholders in assessing their quality and suitability. The known caveats and limitations of these experimental statistics are summarised below.

Urban Centre and Population Estimates:

· Population estimates are derived from data using a hybrid method of satellite imagery and national censuses. The alignment of national census boundaries to gridded estimates introduce measurement errors, particularly in newer housing and built-up developments. See section 2.5 of the GHSL technical report release 2023A for more details.

Public Transit Schedule Data (GTFS):

· Does not include effects due to delays (such as congestion and diversions).

· Common GTFS issues are resolved during preprocessing where possible, including removing trips with unrealistic fast travel between stops, cleaning IDs, cleaning arrival/departure times, route name deduplication, dropping stops with no stop times, removing undefined parent stations, and dropping trips, shapes, and routes with no stops. Certain GTFS cleaning steps were not possible in all instances, and in those cases the impacted steps were skipped. Additional work is required to further support GTFS validation and cleaning.

Transport Network Routing:

· “Trapped” centroids: the centroid of destination cells on very rare occasions falls on a private road/pathway. Routing to these cells cannot be performed. This greatly decreases the transport performance in comparison with the neighbouring cells. Potential solutions include interpolation based on neighbouring cells or snapping to the nearest public OSM node (and adjusting the travel time accordingly). Further development to adapt the method for this consideration is necessary.

Please also visit the Python package and Docker Image GitHub issues pages for more details.

How to Contribute:

We hope that the public, other public sector organisations, and National Statistics Institutions can collaborate and build on these data, to help improve the international comparability of statistics and enable higher frequency and more timely comparisons. We welcome feedback and contribution either through GitHub or by contacting datacampus@ons.gov.uk.

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by distance travelled to work and by age. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Estimates for single year of age between ages 90 and 100+ are less reliable than other ages. Estimation and adjustment at these ages was based on the age range 90+ rather than five-year age bands. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, data is available by:

country - for example, Wales region - for example, London local authority - for example, Cornwall health area – for example, Clinical Commissioning Group statistical area - for example, MSOA or LSOA

Distance travelled to work

The distance, in kilometres, between a person's residential postcode and their workplace postcode measured in a straight line. A distance travelled of 0.1km indicates that the workplace postcode is the same as the residential postcode. Distances over 1200km are treated as invalid, and an imputed or estimated value is added.

“Work mainly at or from home” is made up of those that ticked either the "Mainly work at or from home" box for the address of workplace question, or the “Work mainly at or from home” box for the method of travel to work question.

Distance is calculated as the straight line distance between the enumeration postcode and the workplace postcode.

Combine this variable with “Economic activity status” to identify those in employment at the time of the census.

Age

A person’s age on Census Day, 21 March 2021 in England and Wales. Infants aged under 1 year are classified as 0 years of age.

The data for the research project SCORE – Sub-national Context and Radical Right Support in Europe was collected in 2017. The research project focuses on analysing the causes and effects of sub-national differences in support for the radical right in Germany with the aim of improving the existing patterns. The survey evaluated opinions and attitudes on a range of topics, including Euroscepticism, right-wing populism, attitudes towards Islam, globalisation, political identification and participation. The dataset was collected as part of the German component of the transnational project Sub-National Context and Radical Right Support in Europe (SCoRE), which involves France, Germany, the Netherlands and the United Kingdom. The online survey was conducted by infratest dimap (N=25976).

Another aim of the survey is to analyse the data at a small-scale regional level. To this end, the datasets were categorised into a regional structure during data processing. Based on the respondents´ address data, geocodes (meridians and parallel arcs) were assigned to each data set using the offline software Map&Market premium. The data records were located within a regional radius defined by the Federal Statistical Office on the basis of the previously determined geocodes. For this purpose, a number of 362,000 grid cells with a size of one square kilometre were used. Grid cells in which the number of respondents was less than six were aggregated to avoid the possibility of re-identification. In accordance with the provisions of the German Data Protection Act, address data and survey data were kept separate at all times during the process and all process steps were supervised by infratest dimap´s data protection officer. In particular, data protection was ensured by mapping addresses and geocodes completely offline. The sample was adjusted to the demographic structures of the universe derived from official statistics. The current population extrapolation and the current “Mikrozensus” of the Federal Statistical Office were used as the data basis. Population distributions are generally adjusted for regional criteria such as Nielsen regions and municipality size classes (BIK10), as well as region (East/West), age groups, gender and educational attainment. Further information can be found at: https://www.score.uni-mainz.de/
Right-left political self-assessment; satisfaction with local politics; interest in politics; understanding of politics and political effectiveness; far-right/populist attitudes; scepticism towards the European Union (EU), attitudes towards EU membership; perceived cultural threat from immigration; increase in crime due to immigration; perceived economic threat from immigration; threat to welfare state due to immigration; party voting; authoritarian attitudes; prejudices against Islam; conservative attitudes and values; traditional gender roles, homosexuality; perception of ethnic diversity in neighbourhood; Intergroup contact, contact with ethnic minority groups in everyday life, relationship with these groups, integration of ethnically diverse groups in circle of friends and family; party identification; economic right-left assessment; attitudes towards globalisation; welfare chauvinism; media consumption, news; relative deprivation; life satisfaction; Moreno scale (attachment to place of residence, region, federal state, country, EU); Housing situation; distance between place of birth and current place of residence; infrastructure and environmental aspects; local disintegration; perception of changes in the neighbourhood; feeling of regional exclusion, segregation, marginalisation; voluntary work; political participation; turnout and voting behaviour in the last federal election. Demographics: gender; year of birth; age; education; employment; occupational status; federal state; nationality; place of birth; parents´ place of birth; religious affiliation.

Georeferenced data: Data were located within a regional radius provided by the Federal Statistical Office using geocodes (grid cells).

Additionally coded: Interrogator (device).

This dataset provides Census 2021 estimates that classify usual residents in England and Wales by distance travelled to work and by occupation. The estimates are as at Census Day, 21 March 2021.

As Census 2021 was during a unique period of rapid change, take care when using this data for planning purposes. Read more about this quality notice.

Area type

Census 2021 statistics are published for a number of different geographies. These can be large, for example the whole of England, or small, for example an output area (OA), the lowest level of geography for which statistics are produced.

For higher levels of geography, more detailed statistics can be produced. When a lower level of geography is used, such as output areas (which have a minimum of 100 persons), the statistics produced have less detail. This is to protect the confidentiality of people and ensure that individuals or their characteristics cannot be identified.

Lower tier local authorities

Lower tier local authorities provide a range of local services. There are 309 lower tier local authorities in England made up of 181 non-metropolitan districts, 59 unitary authorities, 36 metropolitan districts and 33 London boroughs (including City of London). In Wales there are 22 local authorities made up of 22 unitary authorities.

Coverage

Census 2021 statistics are published for the whole of England and Wales. However, data is available by:

country - for example, Wales region - for example, London local authority - for example, Cornwall health area – for example, Clinical Commissioning Group statistical area - for example, MSOA or LSOA

Distance travelled to work

The distance, in kilometres, between a person's residential postcode and their workplace postcode measured in a straight line. A distance travelled of 0.1km indicates that the workplace postcode is the same as the residential postcode. Distances over 1200km are treated as invalid, and an imputed or estimated value is added.

“Work mainly at or from home” is made up of those that ticked either the "Mainly work at or from home" box for the address of workplace question, or the “Work mainly at or from home” box for the method of travel to work question.

Distance is calculated as the straight line distance between the enumeration postcode and the workplace postcode.

Combine this variable with “Economic activity status” to identify those in employment at the time of the census.

Occupation (current)

Classifies what people aged 16 years and over do as their main job. Their job title or details of activities they do in their job and any supervisory or management responsibilities form this classification. This information is used to code responses to an occupation using the Standard Occupational Classification (SOC) 2020.

It classifies people who were in employment between 15 March and 21 March 2021, by the SOC code that represents their current occupation.

The lowest level of detail available is the four-digit SOC code which includes all codes in three, two and one digit SOC code levels.

Abstract

The Central Statistical Office carried out a Living Conditions Monitoring Survey in November-December, 1998. The survey was carried out nation-wide in all the 72 districts of Zambia on a sample basis. The main objectives of the survey are to:- (i) Monitor the effects of government policies on households and individuals. (ii) Measure and monitor poverty overtime in order for government to evaluate its poverty reduction programs. (iii) To monitor the living conditions of households in Zambia in the form of access to various economic and social facilities and infrastructure and access to basic needs; food, shelter, clean water and sanitation, education and health, etc. (iv) To identify vulnerable groups in society. The Living Conditions Monitoring Survey (LCMS 1998) collected data on the living standards of households and persons in the areas of education, health, income sources, income levels, food production and consumption, and access to various amenities.

Geographic coverage

National

Analysis unit

• Households
• Individuals

Universe

The LCMS 1998 was conducted nation-wide on a sample basis and covered both rural and urban areas of all the 72 districts in the country. The eligible household population consisted of all households. Excluded from the sample were institutional populations in hospitals, boarding schools, colleges, universities, prisons, hotels, refugee camps, orphanages, military camps and bases and diplomats accredited to Zambia in embassies and high commissions. Private households living around these institutions and cooking separately were included such as teachers whose houses are within the premises of a school, doctors and other workers living on or around hospital premises, police living in police camps in separate houses, etc. Persons who were in hospitals, boarding schools, etc. but were usual members of households were included in their respective households. Ordinary workers other than diplomats working in embassies and high commissions were included in the survey also. Others with diplomatic status working in the UN, World Bank etc. were included. Also included were persons or households who live in institutionalized places such as hostels, lodges, etc. but cook separately. The major distinguishing factor between eligible and non eligible households in the survey is the cooking and eating separately versus food provided by an institution in a common/communal dining hall or eating place. The former cases were included while the latter were excluded.

Kind of data

Sample survey data [ssd]

Sampling procedure

Sampling Frame and Stratification The country is made up of 9 provinces comprising 72 districts delineated by the Local Government Administration. Previously there were 57 districts in Zambia. 15 new districts have been created. Central Statistical Office has delineated these districts into Census Supervisory Areas (CSAs) and then these into Standard Enumeration Areas (SEAs) for the purposes of conducting censuses and sampling for surveys. Each CSA is made up about 3 SEAs. The list of CSAs and SEAs by province & district constitute the sampling frame for CSO censuses and surveys. The sampling frame comprises 4,193 CSAs of which 3,231 are rural and 962 are urban and 12,999 SEAs. The frame of CSAs and SEAs is arranged by province, then by district within a province,then by rural/urban within a district, then by centrality within rural/urban, and finally by low, medium or high cost for urban SEAs. The frame also contains information on the number of households and the population size per SEA and this is what was used when selecting the sample using the probability proportional to size (PPS) method. The number of households and the population in the frame is based on the 1990 population census. To boost the data from the survey to 1998 population parameters the weights calculated were multiplied by a factor equal to the estimated population growth from 1990 to 1998. This was done at the district level.

The classification of centrality is shown below:- Centrality Classification:- 1. Areas within Lusaka city. 2. Areas within Ndola city. 3. Areas within Kitwe city. 4. Areas within 50 Kms radius outside Lusaka, or Ndola, or Kitwe cities. 5. Areas within provincial capitals. 6. Areas along Southern to Copperbelt line of Rail (within 30 Kms radius). 7. Areas along Northern line of Rail (within 30 Kms radius). 8. Areas within 30kms radius outside provincial capitals. 9. Areas within district centres. 10.Areas within 30 Kms radius outside district centres 11.Remote areas.

Areas within cities, provincial capitals and district centres is equivalent to the urban part of the town.Within the rural SEAs households have been classified on the basis of the scale of agricultural activities into small scale, medium scale, large scale, and non-agricultural households.The urban SEAs have been classified into low cost, medium cost or high cost depending on the type of housing in the area.The local government administration has classified localities into low, medium and high cost based on the required housing standard. The urban SEAs were classified into low, medium and high cost areas based on a combination of the local government and CSO criteria. All urban SEAs were physically visited by CSO mapping staff with locality classification from local government and determined whether the SEA was low, medium or high cost based on the local government definition and the actual observation of the mapper. The mappers were trained on how to make this determination. Households within rural SEAs were classified into small scale, medium scale, large scale, and non agricultural households after the listing operation.

Sample Size: Out of a total of 12,999 SEAs in the frame, a sample of 820 SEAs were selected for the Living Conditions Monitoring Survey (1998) representing about 6% of the total. The urban stratum was allocated 328 SEAs and the rural stratum was allocated 492 SEAs. The total number of households enumerated were 8520 in rural areas and 8220 in the urban areas.The total number of persons who fell in the sample were 45989 in rural areas and 47480 in urban areas.All the 72 districts in Zambia were covered in the survey on a sample basis.

Sample Allocation: Sample allocation was done using the "Probability Proportional to size" (PPS) method. This entailed allocating the total sample (820) proportionately to each province according to its population share.Thereafter, allocation of the provincial sample was done proportionately to each district according to the population share from the provincial population. Similarly allocation was done by centrality within a district. For example, Mkushi district was allocated 10 SEAs by the PPS method. The district has four centrality classifications (9, 7, 10, and11). The number of SEAs under each centrality classification in the frame were summed up. The next step was to determine the share of each centrality group of SEAs from the total number of SEAs in the frame under Mkushi district. The corresponding proportions were used to allocate the sample to each centrality category. However, the final allocation was plus or minus depending on what was obtaining in the frame. For example if 1 SEA was to be allocated to centrality 9 (District centre) by using PPS and yet there is low, medium & high cost SEAs under centrality 9 in that district, the number of SEAs selected was 3 (one from low, and the other two from the medium & high cost SEAs). Not all centrality classifications obtain in all districts, for example, Lusaka district had all the SEAs fall under centrality 1 (Lusaka city) in the frame. Therefore the entire number of SEAs allocated to Lusaka district was selected from this category. The minimum size for each district sample was 7 SEAs, meaning that even the smallest district was allocated at least 7 SEAs.

Sample Selection: Sample selection was done in two stages. In the first stage, a sample of SEAs was selected within each stratum (centrality) according to the number allocated to that stratum. The second stage comprised selection of households from each sample SEA according to the number of households recommended after a complete listing of all households in the sample SEAs. Thus SEAs formed primary sampling units. The unit of analysis was the household.

Selection of SEAS: After sample allocation was done, selection of the sample SEAs from the frame followed. The allocated number of SEAs were selected at centrality level using the PPS method.

Selection of Households:In each selected SEA, households were listed and each household given a unique sampling serial number. A circular systematic sample of households was then selected. Vacant residential housing units and noncontact households were not assigned sampling serial numbers. Selection of sample households was done by supervisors in the field and they were required to select the following numbers of households: 30 households from SEAs with sample Micro-projects (whether rural or urban). 25 households from urban SEAs (without sample micro-projects) 15 households from rural SEAs (without sample micro-projects). This number increased in rural SEAs where large scale farmers were identified.

In urban areas the required sample number of households were selected straight forwardly using the circular systematic sampling method. In the rural areas, 7 households were selected from the stratum of small scale farmers, 5 from medium scale farmers, 3 from non-agricultural households, and all large scale farmers if any were found in the SEA. Therefore, the number of selected households from a rural SEA was more than 15 where there were large scale farmers. In Micro-project areas the number of households to