Analysis of ‘MIO MPC Zones’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5fd812ab-f883-4397-bbbd-c5ab727a3bf5 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

The purpose of developing parcel-referenced digital zoning dataset is to provide an accurate information about the zoning regulations and designations within Somerset County, New Jersey. The dataset depicts boundaries and attributes of different zoning districts, such as residential, commercial, industrial, agricultural, and mixed-use zones. By utilizing the GIS dataset, urban planners, developers, policymakers, and other stakeholders can visualize and analyze the spatial distribution of zoning regulations. It helps them understand the existing land-use patterns, identify suitable locations for various types of development, evaluate compliance with zoning regulations, and make informed decisions regarding land use planning, development proposals, and policy changes. Additionally, combining zoning data with other spatial datasets, such as transportation networks, population demographics, or environmental data, can provide valuable insights for comprehensive planning and decision-making processes.

Zoning is a tool used by the City of Los Angeles to designate, regulate and restrict the location and use of buildings, structures and land, for agriculture, residence, commerce, trade, industry or other purposes; to regulate and limit the height, number of stories, and size of buildings and other structures hereafter erected or altered to regulate and determine the size of yards and other open spaces and to regulate and limit the density of population; and for said purposes to divide the City into zones of such number, shape and area as may be deemed best suited to carry out these regulations and provide for their enforcement. Further, such regulations are deemed necessary in order to encourage the most appropriate use of land; to conserve and stabilize the value of property; to provide adequate open spaces for light and air, and to prevent and fight fires; to prevent undue concentration of population; to lessen congestion on streets; to facilitate adequate provisions for community utilities and facilities such as transportation, water, sewerage, schools, parks and other public requirements; and to promote health, safety, and the general welfare all in accordance with the comprehensive plan.

TAZ 2010 shapefile - Traffic Analysis Zones boundaries. TAZs are the geographies used in the travel demand modeling process. Demographic inputs are supplied at the TAZ level and the resulting model outputs include zone-to-zone trip tables and time or distance matrices. The TAZ 2010 system has 3700 zones and was constructed using aggregations of 2010 Census Blocks. It is the current TAZ system.

Analysis of ‘Historic Special Review District’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/f2daba59-8a90-4b16-a2f6-c65acf633c45 on 27 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

Montgomery County is currently divided into 376 geographically distinct areas defined by natural or man-made features. Socio-economic and demographic data for each zone are analyzed to help estimate transportation demand.For more information, contact: GIS Manager Information Technology & Innovation (ITI) Montgomery County Planning Department, MNCPPC T: 301-650-5620

The baseline survey in Tajikistan captures data in the Feed the Future Zones of Influence (ZOI), comprised of 12 of the 24 districts in Khatlon province. A total of 2,000 households in the ZOI were surveyed for the PBS data collection activity. These households are spread across 100 standard enumeration areas in the targeted districts. The survey is comprised of ten CSV files: a children's file, a household-level file, a household member level file, a women's file, several files describing consumption, and two files used to construct the Women's Empowerment in Agriculture Index. This dataset contains data from sub-Module E1: Food Consumption over the Past 7 Days.

Analysis of ‘Potentially Redevelopable’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/9aab0495-7787-497d-b34b-a23414fdeb39 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

Zoning is a locally regulated law that is used as a guideline for land management control and conformity by establishing specific policy that must be followed in the use of land and buildings. Zoning asserts explicit uses that are permitted under varying circumstances. It dictates reasonable development by protecting property from detrimental uses on nearby properties. Zoning also standardizes the size of lots, the building set backs from roads or adjoining property, maximum height of buildings, the population density, and other land use issues.Zoning is used to designate, regulate and restrict the location and use of buildings, structures and land, for agriculture, residence, commerce, trade, industry or other purposes; to regulate and limit the height, number of stories, and size of buildings and other structures hereafter erected or altered to regulate and determine the size of yards and other open spaces and to regulate and limit the density of population; and for said purposes to divide the City into zones of such number, shape and area as may be deemed best suited to carry out these regulations and provide for their enforcement. These regulations are deemed necessary in order to encourage the most appropriate use of land; to conserve and stabilize the value of property; to provide adequate open spaces for light and air, and to prevent and fight fires; to prevent undue concentration of population; to lessen congestion on streets; to facilitate adequate provisions for community utilities and facilities such as transportation, water, sewerage, schools, parks and other public requirements; and to promote health, safety, and the general welfare all in accordance with the comprehensive plan.For more information, please refer to Section 12.04 of the Los Angeles Planning and Zoning Municipal Code and the Generalized Summary of Zoning Regulations, City of Los Angeles.Refresh Rate: Monthly

Linear regression between the economic development levels and land use types.

Shows the acreage of "general" zoning categories in each neigborhood reporting area. Neigborhood reporting areas are a combination of official and unofficial boundaries for the purpose of collecting and reporting information (data) in Austin. They are comprised of Neighborhood Planning Areas (in the central core) which are approved and can only be changed by City Council. Areas outside of neighborhood planning areas were drawn using logical boundaries such as roadways, and covering larger areas encompassing several neighborhoods. A Neighborhood Reporting Area map is available at http://www.austintexas.gov/sites/default/files/files/Planning/Demographics/Neighborhood_Reporting_Areas.pdf. The zoning data does not indicate public right-of-way (ROW) areas, such as streets and railroad ROW's, which are not typically zoned. General zoning includes major base zone district categories plus zones with vertical and mixed use overlays. Zoning maps are available at http://austintexas.gov/page/planning-maps, and http://www.arcgis.com/home/item.html?id=6803413bed5e4aa0bb13c93c71ccb41d. More information on zoning is available at http://www.austintexas.gov/department/zoning. This information is taken from the zoning layer https://data.austintexas.gov/Geodata/Zoning/5rzy-nm5e

Demographic factors such as migration rate and population size can impede or facilitate speciation. In hybrid zones, reproductive boundaries between species are tested and demography mediates the opportunity for admixture between lineages that are partially isolated. Genomic ancestry is a powerful tool for revealing the history of admixed populations, but models and methods based on local ancestry are rarely applied to structured hybrid zones. To understand the effects of demography on ancestry in hybrids zones, we performed individual-based simulations under a stepping-stone model, treating migration rate, deme size, and hybrid zone age as parameters. We find that the number of ancestry junctions (the transition points between genomic regions with different ancestries), as well as heterogenicity (the genomic proportion heterozygous for ancestry), are often closely connected to demographic history. Reducing deme size reduces junction number and heterogenicity. Elevating migration increases heterogenicity, but migration affects junction number in more complex ways. We highlight the junction frequency spectrum as a novel and informative summary of ancestry that responds to demographic history. A substantial proportion of junctions are expected to fix when migration is limited or deme size is small, changing the shape of the spectrum. Our findings suggest that genomic patterns of ancestry could be used to infer demographic history in hybrid zones.

Our product delivers insights into foot traffic and spend patterns across key urban areas globally, utilising H3 hexbins at Level 12 resolution. By leveraging our advanced location signals technology, which captures real-time, anonymised movement data, we provide a comprehensive understanding of how people interact with urban spaces. With a client base of over 600 satisfied customers, our data stands as a trusted resource for making informed decisions in urban development and planning on a global scale.

Key Features and Specifications

Valued by 600+ Customers: Our data is trusted by a substantial client base, proving its reliability and utility across various urban planning needs around the world.

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Catchment Area Analysis: Understand the reach and influence of different urban locations with our catchment area analysis. This feature helps planners and policymakers determine how far people are willing to travel to access certain services or amenities, essential for infrastructure development and resource allocation.

Density Maps: Visualise population density and movement patterns with our detailed density maps. These maps provide critical insights into where people congregate within urban spaces, helping planners make informed decisions about zoning, public space design, and resource distribution.

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Why Choose Huq Industries' Footfall Data for Urban Planning?

Urban Development

For urban planners, our global footfall data, powered by location signals, is a critical tool for understanding how people interact with urban spaces across different regions of the world. By analyzing foot traffic patterns, including daypart footfall, planners can make informed decisions about infrastructure development, public transportation, and the allocation of resources, regardless of location. The granularity and frequency of our data enable detailed analysis, essential for designing cities that meet the needs of their diverse populations.

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Our data, enhanced by location signals and density maps, offers invaluable insights into the usage of public spaces, allowing planners to manage and improve urban areas effectively, whether in bustling metropolises or smaller cities. By tracking visitor numbers, demographics, and movement patterns globally, planners can optimize the design and functionality of parks, squares, and other public spaces to enhance community well-being and accessibility.

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Transportation planners benefit from our data by leveraging it for comprehensive transportation network analysis worldwide. The integration of location signals and daypart footfall analysis provides real-time insights into pedestrian movement across different regions, enabling accurate forecasting of transportation needs and the development of efficien...

This layer shows population, household, and employment forecasts at the TAZ level. Anne Arundel County data includes the City of Annapolis. Forecasts endorsed by the Baltimore Regional Transportation Board on July 28, 2020.The mission of the Cooperative Forecasting Group (CFG) is to develop a set of population, household, and employment control totals and small area forecasts to be used for transportation planning. The data set is utilized at BMC as an input to the travel demand model and for air quality conformity testing, and is available to federal, state, and local government agencies, as well as the general public. The local planning agencies that comprise the CFG are responsible for the development of the estimates and forecasts for their own jurisdictions. While methodologies vary between jurisdictions, the forecasts are generally based on local comprehensive plans, adopted zoning maps and regulations, and an inventory of residential holding capacity. The allocation of employment to the small area and its distribution across industry sectors is based largely upon an inventory of employers across the region.

New forecast rounds are developed on an as needed basis, but are generally triggered by the update of major planning documents by the local jurisdictions, significant unforeseen demographic shifts, and the availability of small area data. The members of the CFG have the opportunity to make annual updates to the cooperative forecasts to account for unexpected changes when a complete new “round” of forecasts is not warranted. For more information, see https://www.baltometro.org.

Date: July 28, 2020 Update: Varies, see above. New forecasts are added as separate Open Data items. Attributes:

Field

Description

TAZ10

2010 BMC traffic analysis zones

COUNTYFP10

County FIPS code

RPD10

2010 BMC regional planning districts

POP15

2015 Population

POP20

2020 Population

POP25

2025 Population

POP30

2030 Population

POP35

2035 Population

POP40

2040 Population

POP45

2045 Population

HH15

2015 Household count

HH20

2020 Household count

HH25

2025 Household count

HH30

2030 Household count

HH35

2035 Household count

HH40

2040 Household count

HH45

2045 Household count

EMP15

2015 Number of jobs

EMP20

2020 Number of jobs

EMP25

2025 Number of jobs

EMP30

2030 Number of jobs

EMP35

2035 Number of jobs

EMP40

2040 Number of jobs

EMP45

2045 Number of jobs

Proportion of population in Pacific Island Countries and Territories (PICTs) living in Low Elevation Coastal Zones (LECZ) of 0-10 and 0-20 meters above sea level. LECZ were delineated using the bathub method overlaid on the Advanced Land Observing Satellite (ALOS) Global Digital Surface Model (AW3D30). Populations within the LECZs were estimated using the Pacific Community (SPC) Statistics for Development Division’s 100m2 population grids.

Find more Pacific data on PDH.stat.

The basin of life is the smallest area in which people have access to the most common facilities and services. Its contour is defined in several stages. First, a service hub is defined as an urban municipality or unit with at least 16 of the 31 intermediate facilities. This range of equipment has been retained because it is not present throughout the territory and therefore has a more structuring role. The areas of influence of each service hub are then demarcated by grouping the nearest municipalities, the proximity being measured in travel time, by road at hollow time. Thus, for each municipality and for each equipment not present in the municipality, the nearest municipality offering this equipment is determined. Intermediate equipment as well as local equipment are taken into account. Successive iterations make it possible to draw the perimeter of the basins of life. Compared to urban area zoning, which measures the influence of cities on the basis of commuting between home and work, zoning into living areas complements the analysis of the distribution of facilities and their access. Its main interest is to describe unpopulated spaces, i.e. living areas built on urban units of less than 50,000 inhabitants. The urban-rural typology is based on the classification now used by the European Commission. From 200 m tiles, urban meshes are formed which meet two conditions: a population density of at least 300 inhabitants per km² and a minimum of 5,000 inhabitants. The other meshes are considered to be rural. Therefore, the basins of life were classified into the following three groups: — urban life basin: the population classified in the urban grids represents more than 80 % of the total population in the living basin; — intermediate life basin: the population classified in the urban grids represents between 50 % and 80 % of the total population in the living basin; — rural living area: the population classified in the urban grids represents less than 50 % of the total population in the living basin. The text contrasts rural life basins with intermediate urban and (inferred) basins of life. Lifeshed zoning has been revised as part of an interdepartmental working group of INSEE; the Interministerial Delegation for Regional Planning and Attractiveness (Datar); the Directorate of Research, Studies, Evaluation and Statistics (Drees) of the Ministry of Social Affairs and Health; the Statistics and Foresight Service (SSP) of the Ministry of Agriculture, Food, Fisheries, Rurality and Spatial Planning; the Department of Local Studies and Statistics of the Directorate-General of Local Authorities at the Ministry of the Interior (DGCL); and the centre for economics and sociology applied to agriculture and rural areas of the National Institute of Agricultural Research (Inra).

Abstract

The major objective of this survey was to provide up-to-date and accurate information on fertility, contraception, child mortality, child nutrition and health status of children.

This sample survey is further intended to serve as a source of demographic data for comparison with earlier surveys such as Sri Lanka Demographic and Health Survey 1987 (DHS87) and Sri Lanka Contraceptive Prevalence Survey 1982 (CPS82). Such comparisons help to understand the demographic changes over a period of time.

Two types of questionnaires were used in the survey. ie (1) Household and (2) Individual.

Source : Report on Sri Lanka Demographic and Health Survey 1993 published in 1995

Geographic coverage

The country has been stratified into nine zones on the basis of socio economic and ecological criteria for DHS87. The same zones were used without major changes. Although there are nine zones the survey was confined to seven excluding Northern and Eastern provinces; the few areas covered in Amparai district in the Eastern Province during DHS87 had to be excluded due to security reasons of the country.

Analysis unit

(1) Household (2) Eligible women (3) Children

Universe

The survey interviews were designed to obtain responses from all usual residents and any visitors who slept in the household the night before the interview. An eligible respondent was defined as an ever married woman aged 15 - 49 years who slept in the household the night before the interview.

Source : Report on Sri Lanka Demographic and Health Survey 1993 published in 1995

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size - 9230 households 7078 eligible women in 9007 housing units.

Selection process : The sample is a multi-stage stratified probability sample representative of the entire country excluding Northern and Eastern Provinces. The country has been stratified into nine zones on the basis of socio-economic and ecological criteria for DHS87. The same zones were used without major changes. Although there are nine zones the survey was confined to seven, excluding Northern and Eastern Provinces. The seven zones are:

Zone 1 - Colombo Metro consisting some urban areas in Colombo and Gampaha District Zone 2 - Colombo feeder areas Zone 3 - South Western coastal low lands Zone 4 - Lower South Central hill country excluding Districts with a concentration of estates Zone 5 - South Central hill country with a concentration of estates Zone 6 - Irrigated dry zone with major or minor irrigation schemes Zone 7 - Rain-fed Dry zone

Each zone was further stratified into three strata - urban, rural and estate sectors. The number of stages of the design and the Primary Sampling Units (PSU) vary according to the sector.

In urban areas PSU is the ward and generally two census blocks have been selected per ward as the second stage unit. The selections were carried out with probability proportional to size(PPS). The number of housing units was taken as the measure of size.

The PSU's were mostly selected from a specially organized frame consisting of wards and Grama Niladhari divisions organized by zone, sector and within sector geographically. The organization provided a better basis for stratification as it is arranged on a geographical basis.

The census blocks were selected from the only frame available from 1981 Census of Population and Housing. The ever married women aged 15-49 found in the selected housing units were interviewed.

In rural areas, Grama Niladhari (GN) division was taken as PSU and generally a single village has been selected per sample GN division with PPS. As such in rural areas villages form effective PSU's. However special steps were taken to merge and divide the villages to deal with areas which are too small or too large.

Unlike the GN divisions and wards, the selection in the estate sector has to take into account the fact that many estates are very small in size to form proper units for first stage of selection. To avoid the need to group estates in the whole frame special procedure was applied to select estates depending on the relative size of the estate compared to the nearby estates.

Sampling deviation

The target sample size was 6500 ever married women in the age group 15-49. This includes an over-sampling of around 500 women in five less developed areas in zones 6 and 7. The latter addition to the sample is needed to provide Policy relevant information and permit comparative analysis of these areas. In order to get that target sample, a total of 9007 housing units were selected for the survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Household Questionnaire - listed all usual residents any visitors who slept in the household the night before the interview and some basic information was collected on the characteristics of each person listed such as age, sex, marital status, relationship to head of household. The household questionnaire was used to identify women who were eligible for the individual questionnaire.

Individual questionnaire - Administered to each eligible woman who was defined as one who is an ever married female aged between 15 - 49 who slept in the household the night before the interview. This questionnaire had eight sections such as Respondent's background, Reproduction, Contraception, Health of children, Marriage, Fertility, Husband's background, length and weight of infants.

Source : Report on Sri Lanka Demographic and Health Survey 1993 published in 1995

Cleaning operations

Manual editing covered basic investigations such as checking of identification details, completeness of the questionnaire, coding, age and birth history, checking of certain internal consistencies, checking the information recorded in filter questions and coding of few items.

Response rate

Sample size - 9230 households 7078 eligible women in 9007 housing units. Completed - 8918 households 6983 eligible women

Household response rate - 98.9% Eligible women response rate - 98.7% Overall response rate - 97.6%

Household interviews

Completed 96.6% other(vacant, incompetent responder, refused etc) 3.4% Un-weighted number 9230

Eligible women interviews

Completed 98.7% Other(not in, refused, partly complete etc) 1.3% Un-weighted number 7078

Sampling error estimates

The sample of women had been selected as a simple sample, it would have been possible to use straightforward formulas for calculating sampling errors. However the sample design for this survey depended on stratification, stages and clusters. The computer package CLUSTERS developed by the International Statistical Institute for the World Fertility Survey was used to assist in computing the sampling errors with the proper statistical methodology.

In general, the sampling errors are small, which implies that the results are reliable.

Data appraisal

Pl refer to the Source : Report on Sri Lanka Demographic and Health Survey 1993 published in 1995

Référentiel géospatial et administratif simple. Ce jeu de données est construit à partir du Code Officiel Géographique de l'INSEE, disponible via leur interface SparQL. Modèle Il y a deux types d'objets: les niveaux les zones Zones Le fichier Zones {année} (json) est construit à partir des données extraites du COG, et contient, pour toutes les échelles géographiques, les informations suivantes : uri : URI de l'entité dans le graphe RDF de l'INSEE (exemple : "http://id.insee.fr/geo/arrondissement/6eeefa75-7352-48ee-884f-64783b8ca290"), nom : nom de l'entité (exemple : "Lyon"), codeINSEE : code INSEE de l'entité (exemple : "691"), nomSansArticle : nom sans article de l'éntité (exemple : "Lyon"), codeArticle : code de l'article de l'entité (exemple : "0"), type : type de l'entité (exemple : "Arrondissement"), is_deleted : booléen indiquant si l'entité a été administrativement supprimée (exemple : true), level : niveau d'échelle de l'entité (exemple : "fr:arrondissement"), _id : identifiant complet utilisé par data.gouv.fr (exemple : "fr:arrondissement:691") Le fichier Zones pays uniquement {année} (json) est un échantillon du fichier global Zones {année} (json) qui ne contient que les pays. Niveaux/Levels Le fichier contient les différents niveaux d'échelles possible, avec les informations suivantes : id : niveau d'échelle de l'entité, qui correspond au champ level du fichire Zones (exemple : "fr:region"), label : appellation du niveau d'échelle (exemple : "French region"), admin_level : code du niveau d'échelle (exemple : 40), parents : niveau(x) d'échelle directement supérieur(s) (exemple : ["country"]) Construction Ce jeu de données est construit à partir du COG de l'INSEE via un script python disponible ici. Historique 30/04/2015 : première version 15/04/2016 : ajout des URL des blasons/drapeaux ainsi qu'un export utilisant msgpack afin de réduire la taille de l'archive générée 19/04/2016 : version de correction fournissant un découpage plus fin des formes des communes 09/06/2016 : version de correction ajoutant les parents pour les communes de Corse/DROM-COM et calculant la population pour les districts 15/06/2017 : version incluant les données issues de GeoHisto et utilisant des GeoIDs, intègre les données 2017 (COG, OSM). 28/08/2017 : Ajout de l'historique des EPCIs issue de GeoHisto. 08/05/2019 : Passage au COG 2019, correction de bugs, ajout de la clé geonames, passage à Wikidata, les cantons et les iris ne sont plus exportés 30/11/2023 : Les données sont issues du COG de l'INSEE à partir de leur interface SparQL Archives Niveaux/Levels Ils permettent de modéliser les différents niveaux connus du référentiel et leur relations théoriques. Leur nom est traductible. Zones Une zone est l'association d'un identifiant unique à polygone géographique, un niveau et un nom. Il a moins un code unique pour le niveau. Il peut avoir plusieurs identifiants connus, qui ne sont pas nécessairement uniques. Le nom est optionnellement traductible (ex: Union européenne, Monde) Les attributs suivant sont exportés dans le GeoJSON: id : Un identifiant unique suivant la spécification GeoID code : L'identifiant unique pour une date donnée de la zone pour son niveau level : L'identifiant du niveau de rattachement name : Le nom d'affichage de la zone en anglais (peut-être traduit) population : La population approximative/estimée (optionnel) area : L'aire estimée/approximative en km2 (optionnel) wikidata : Le noeud Wikidata associé (optionnel) wikipedia : Une référence vers Wikipedia (optionnel) dbpedia : Une référence vers DBPedia (optionnel) flag : Une référence vers le drapeau DBPedia (optionnel) blazon : Une référence vers le blazon DBPedia (optionnel) keys : un dictionnaire des différents code connus pour cette zone parents : une liste non-ordonnée des identifiants des différents parents connus ancestors : la liste des éventuels ancêtres successors : la liste des éventuels successeurs validity: une période de validité (objet ayant les attributs start/end) (optionnel) Construction Ce jeu de données est construit avec l'outil GeoZones dont le code est publié sur Github. Vous pouvez retrouver le détail des spécificités françaises sur le dépôt. Améliorations futures possibles Champs Poids global = f(population, area, level) Livrables Différentes précisions JSON localisés (en anglais seulement pour l'instant) Traductions en JSON (comme alternative dur format PO/MO actuel) Statistiques des niveaux (nombre de zones, couverture des attributs...)

Since there was no dedicated Population Based Survey to generate the baseline values, this analysis uses two distinct datasets: (a) Cambodia Socioeconomic Survey (CSES, 2009); (b) Cambodia Demographic and Health Survey (CDHS, 2010). Data was extrapolated and created using the following methodology: (a) Cambodia Socioeconomic Survey (CSES): the Cambodia Socioeconomic Survey (CSES, 2009) was conducted by the National Institute of Statistics (NIS) of the Ministry of Planning (MOP) of Cambodia. The CSES 2009 was a nationally representative survey with a sample of 12,000 households within 720 sampling units (villages), which were divided into 12 monthly samples of 1000 households in 60 villages. The sampling design provided for estimates for urban and rural areas and the Municipality of Phnom Penh. The 2008 Population Census of Cambodia was used as sampling frame (NIS, 2010). The sampling design in the CSES 2009 survey is a threestage design. In stage one, a sample of villages is selected using systematic sampling. In stage two, an Enumeration Area (EA) is selected from each village selected in stage one using Simple Random Sampling (SRS). Finally, in stage three, a sample of households is selected from each EA by systematic sampling. For the generation of the relevant Baseline indicators we used the sample for the four FtF provinces, with a total of 2,453 households (2,096 in rural and 357 in urban areas). The CSES collects a wide range of data related to household living conditions, income generation and expenditures. (b) Demographic and Health Survey (CDHS): The Cambodia Demographic and Health Survey (CDHS, 2010) is a nationally representative sample survey of 18,754 women and 8,239 men age 1549. The 2010 CDHS is the third comprehensive survey conducted in Cambodia as part of the worldwide MEASURE DHS project. The primary purpose of the CDHS is to provide policymakers and planners with up-to-date, reliable data on fertility; family planning; infant, child, and maternal mortality; maternal and child health; nutrition; malaria; knowledge of HIV/AIDS, and women’s status. The sampling frame used for the 2010 CDHS was the complete list of all villages enumerated in the 2008 Cambodia General Population Census provided by the NIS. The survey was based on a stratified sample selected in two stages. In the first stage, 611 EAs were selected with probability proportional to size. The household listings provided the frame from which households were selected in the second stage. To ensure a sample size large enough to calculate reliable estimates for each study domain, it was necessary to restrict the total number of households selected to 24 in each urban EA and 28 in each rural EA. For the purposes of generating the Cambodia Feed the Future Indicators, we use the CDHS 2010 survey sample for the four provinces that make the Zone of Influence, in which 1,814 were respondents of all nonpregnant women in reproductive age (1549 years); 1,796 women in reproductive age with anemia measurement (1549 years); 78 children 05 months; 736 children 059 months; 634 children 659 months with hemoglobin measurement.