Metadata record for data from ASAC Project 2792 See the link below for public details on this project.

Australia's Census of Antarctic Marine Life project.

This project is a part of the international "Census of Antarctic Marine Life" (CAML) which is to be conducted during the International Polar Year. It is a collaborative contribution by Australia and France to understand the biodiversity of the oceans surrounding Antarctica, with particular emphasis on the fishes of the eastern part of the Australian Antarctic Territory. The biodiversity data, when added to that obtained by all other nations participating in the CAML, will serve as a robust reference for future examinations of the health of the Southern Ocean, and assist in the conservation and management of the region.

2007/2008 Season

A. Plankton 1. The impact of climate change on the plankton. The pelagic ecosystem in the Southern Ocean has taken the brunt of human impact in the region and there is evidence that it is already responding to the effects of global climate change. Plankton is particularly sensitive to climate change and change in their biodiversity is expected to have serious ramifications through the rest of the ecosystem including the survival of higher predators. Some species are adapted to cold waters of Antarctic where some are supposedly cosmopolitan. Which will survive global warming? For how long will there be an Antarctic marine ecosystem? 2. Consequences of environmental change driven by past and current exploitation of living resources in the region, e.g. current scale fish and krill fisheries, fishery by-catch species, recovery of whales and seals. 3. "Ecosystem services" - The role of Southern Ocean plankton as source of human food (krill fishery or other) carbon draw down/mediation, bio-climate feedback though dimethyl sulphide production, bioproducts, sensitive indicators of ocean health, and foundation of the Antarctic marine ecosystem - no plankton, no ecosystem. B. Fish 1. What is the composition of the epipelagic, mesopelagic and benthic ichthyofaunas between the Antarctic Divergence and the coast at Dumont d'Urville? 2. How does the physical and biological structure of the water column, conditions of ice-cover and bottom topography influence the composition and distribution of these ichthyofaunas? 3. What changes in the community structure of the benthic ichthyofauna as a result from the passage of large icebergs? C. Benthos 1. What are the ecological and historical factors affecting benthic diversity? 2. How will benthic communities respond to change? We do not know how sensitive the Antarctic benthic communities are to global climate change, or to localised environmental change as seen in the Antarctic peninsula area, or to the impacts of increased trawling. We have no benchmark to compare the effects of change, although the effects of iceberg scouring and rate of recovery/re-colonisation will serve as a useful analogy for trawling perturbation. 3. What are the links between Antarctic and other faunas? This includes benthic-pelagic coupling, the benthos as a foraging zone for higher predators, and through the Antarctic Circumpolar Current - connections with other southern continents. Field sampling for this project was undertaken in the 2007/08 season, commencing in December and finishing in February 2008. Consequently, sample processing has only been underway for one or two months for plankton and pelagic fish samples. The demersal fish and benthic samples have only recently arrived at the National Natural History Museum (MNHN) in Paris ready for distribution to taxonomists and analysts. However, key CEAMARC collaborators who attended the recent post-field season CEAMARC workshop, Calvi April 2008, agreed that the use of three vessels for the field programme, instead of one ship as originally proposed, more than met expectations should sufficiently address all the objectives. Specifically, we have collected a substantial number of samples with sufficient sampling intensity and resolution to set the required benchmark of biodiversity in the survey for the pelagic, mesobathypelagic and benthic environments. This biodiversity benchmark will allow us to: - Compare changes in biodiversity with future CAML surveys and also with past surveys - Define legacy sites in the survey area for future CAML surveys and interim annual or biennial monitoring programmes to continuing the effects of climate change - Which species are most likely to be affected by climate change and those most likely to survive - Contribute to models looking at long term changes in species composition, ecosystem structure and function, survivorship of key species, effects of global warming, ocean acidification, and impacts on ecosystem service - Studies of the impact of trawling and iceberg scouring on the benthic and demersal communities - Compare pelagic, demersal and benthic communities in the survey area with those in the other CAML survey areas around Antarctica Sufficient samples of plankton, fish and benthos were also collected for genetic and molecular analyses to improve our taxonomic knowledge and address the CAML objective on understanding species radiation.

Taken from the 2008-2009 Progress Report: Public summary of the season progress: This project is a part of the international "Census of Antarctic Marine Life" (CAML) conducted during International Polar Year. It is a collaborative contribution by Australia, France, Japan and Belgium to understand the biodiversity of Antarctic waters, with particular emphasis on plankton, fish and benthos of eastern Antarctica. In 2007/08, three ships surveyed this area with a range of traditional and modern sampling gear. The biodiversity data from this survey will be added to other CAML projects to serve as a robust reference for future examinations of the health of the Southern Ocean, and assist in its conservation and management.

VITAL SIGNS INDICATOR
Housing Permits (LU3)

FULL MEASURE NAME
Permitted housing units

LAST UPDATED
February 2023

DESCRIPTION
Housing growth is measured in terms of the number of units that local jurisdictions permit throughout a given year. A permitted unit is a unit that a city or county has authorized for construction.

DATA SOURCE
California Housing Foundation/Construction Industry Research Board (CIRB) - https://www.cirbreport.org/
Construction Review report (1967-2022)

Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) - https://data.bayareametro.gov/Development/HCD-Annual-Progress-Report-Jurisdiction-Summary/nxbj-gfv7
Housing Permits Database (2014-2021)

Census Bureau Building Permit Survey - https://www2.census.gov/econ/bps/County/
Building permits by county (annual, monthly)

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Bay Area housing permits data by single/multi family come from the California Housing Foundation/Construction Industry Research Board (CIRB). Affordability breakdowns from 2014 to 2021 come from the Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) Housing Permits Database.

Single-family housing units include detached, semi-detached, row house and town house units. Row houses and town houses are included as single-family units when each unit is separated from the adjacent unit by an unbroken ground-to-roof party or fire wall. Condominiums are included as single-family units when they are of zero-lot-line or zero-property-line construction; when units are separated by an air space; or, when units are separated by an unbroken ground-to-roof party or fire wall. Multi-family housing includes duplexes, three-to-four-unit structures and apartment-type structures with five units or more. Multi-family also includes condominium units in structures of more than one living unit that do not meet the single-family housing definition.

Each multi-family unit is counted separately even though they may be in the same building. Total units is the sum of single-family and multi-family units. County data is available from 1967 whereas city data is available from 1990. City data is only available for incorporated cities and towns. All permits in unincorporated cities and towns are included under their respective county’s unincorporated total. Permit data is not available for years when the city or town was not incorporated.

Affordable housing is the total number of permitted units affordable to low and very low income households. Housing affordable to very low income households are households making below 50% of the area median income. Housing affordable to low income households are households making between 50% and 80% of the area median income. Housing affordable to moderate income households are households making below 80% and 120% of the area median income. Housing affordable to above moderate income households are households making above 120% of the area median income.

Permit data is missing for the following cities and years:
Clayton, 1990-2007
Lafayette, 1990-2007
Moraga, 1990-2007
Orinda, 1990-2007
San Ramon, 1990

Building permit data for metropolitan areas for each year is the sum of non-seasonally adjusted monthly estimates from the Census Building Permit Survey. The Bay Area values are the sum of the San Francisco-Oakland-Hayward MSA and the San Jose-Sunnyvale-Santa Clara MSA. The counties included in these areas are: San Francisco, Marin, Contra Costa, Alameda, San Mateo, Santa Clara, and San Benito.

Permit values reflect the number of units permitted in each respective year. Note that the data columns come from difference sources. The columns (SFunits, MFunits, TOTALunits, SF_Share and MF_Share) are sourced from CIRB. The columns (VeryLowunits, Lowunits, Moderateunits, AboveModerateunits, VeryLow_Share, Low_Share, Moderate_Share, AboveModerate_Share, Affordableunits and Affordableunits_Share) are sourced from the ABAG Housing Permits Database. Due to the slightly different methodologies that exist within each of those datasets, the total units from each of the two sources might not be consistent with each other.

As shown, three different data sources are used for this analysis of housing permits issued in the Bay Area. Data from the Construction Industry Research Board (CIRB) represents the best available data source for examining housing permits issued over time in cities and counties across the Bay Area, dating back to 1967. In recent years, Annual Progress Report (APR) data collected by the California Department of Housing and Community Development has been available for analyzing housing permits issued by affordability levels. Since CIRB data is only available for California jurisdictions, the U.S. Census Bureau provides the best data source for comparing housing permits issued across different metropolitan areas. Notably, annual permit totals for the Bay Area differ across these three data sources, reflecting the limitations of needing to use different data sources for different purposes.

VITAL SIGNS INDICATOR
Housing Permits (LU3)

FULL MEASURE NAME
Permitted housing units

LAST UPDATED
February 2023

DESCRIPTION
Housing growth is measured in terms of the number of units that local jurisdictions permit throughout a given year. A permitted unit is a unit that a city or county has authorized for construction.

DATA SOURCE
California Housing Foundation/Construction Industry Research Board (CIRB) - https://www.cirbreport.org/
Construction Review report (1967-2022)

Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) - https://data.bayareametro.gov/Development/HCD-Annual-Progress-Report-Jurisdiction-Summary/nxbj-gfv7
Housing Permits Database (2014-2021)

Census Bureau Building Permit Survey - https://www2.census.gov/econ/bps/County/
Building permits by county (annual, monthly)

CONTACT INFORMATION
vitalsigns.info@bayareametro.gov

METHODOLOGY NOTES (across all datasets for this indicator)
Bay Area housing permits data by single/multi family come from the California Housing Foundation/Construction Industry Research Board (CIRB). Affordability breakdowns from 2014 to 2021 come from the Association of Bay Area Governments (ABAG) – Metropolitan Transportation Commission (MTC) Housing Permits Database.

Single-family housing units include detached, semi-detached, row house and town house units. Row houses and town houses are included as single-family units when each unit is separated from the adjacent unit by an unbroken ground-to-roof party or fire wall. Condominiums are included as single-family units when they are of zero-lot-line or zero-property-line construction; when units are separated by an air space; or, when units are separated by an unbroken ground-to-roof party or fire wall. Multi-family housing includes duplexes, three-to-four-unit structures and apartment-type structures with five units or more. Multi-family also includes condominium units in structures of more than one living unit that do not meet the single-family housing definition.

Each multi-family unit is counted separately even though they may be in the same building. Total units is the sum of single-family and multi-family units. County data is available from 1967 whereas city data is available from 1990. City data is only available for incorporated cities and towns. All permits in unincorporated cities and towns are included under their respective county’s unincorporated total. Permit data is not available for years when the city or town was not incorporated.

Affordable housing is the total number of permitted units affordable to low and very low income households. Housing affordable to very low income households are households making below 50% of the area median income. Housing affordable to low income households are households making between 50% and 80% of the area median income. Housing affordable to moderate income households are households making below 80% and 120% of the area median income. Housing affordable to above moderate income households are households making above 120% of the area median income.

Permit data is missing for the following cities and years:
Clayton, 1990-2007
Lafayette, 1990-2007
Moraga, 1990-2007
Orinda, 1990-2007
San Ramon, 1990

Building permit data for metropolitan areas for each year is the sum of non-seasonally adjusted monthly estimates from the Census Building Permit Survey. The Bay Area values are the sum of the San Francisco-Oakland-Hayward MSA and the San Jose-Sunnyvale-Santa Clara MSA. The counties included in these areas are: San Francisco, Marin, Contra Costa, Alameda, San Mateo, Santa Clara, and San Benito.

Permit values reflect the number of units permitted in each respective year. Note that the data columns come from difference sources. The columns (SFunits, MFunits, TOTALunits, SF_Share and MF_Share) are sourced from CIRB. The columns (VeryLowunits, Lowunits, Moderateunits, AboveModerateunits, VeryLow_Share, Low_Share, Moderate_Share, AboveModerate_Share, Affordableunits and Affordableunits_Share) are sourced from the ABAG Housing Permits Database. Due to the slightly different methodologies that exist within each of those datasets, the total units from each of the two sources might not be consistent with each other.

As shown, three different data sources are used for this analysis of housing permits issued in the Bay Area. Data from the Construction Industry Research Board (CIRB) represents the best available data source for examining housing permits issued over time in cities and counties across the Bay Area, dating back to 1967. In recent years, Annual Progress Report (APR) data collected by the California Department of Housing and Community Development has been available for analyzing housing permits issued by affordability levels. Since CIRB data is only available for California jurisdictions, the U.S. Census Bureau provides the best data source for comparing housing permits issued across different metropolitan areas. Notably, annual permit totals for the Bay Area differ across these three data sources, reflecting the limitations of needing to use different data sources for different purposes.

Abstract

The purpose of the census was to provide demographic and socio-economic statistics in Uganda. The long term objective of the 2002 census was to maintain approximate decennial censuses and ensure availability of time series population benchmark statistical information at various administrative levels for the development of a coordinated and integrated data collection system in the country.The enumeration covered all persons resident in Uganda on the census night. Special arrangements were made to enumerate institutional, homeless, hotel and mobile populations. The census collected data on the demographic and socio-economic characteristics of the population; household and housing conditions, agriculture, activities of micro and small enterprises and the community characteristics.

The main purpose of the Agricultural module was to provide appropriate sampling frames for a detailed Census of Agriculture in 2003, and a Census of Livestock in 2004. There was evidence of deliberate falsification of data from Kotido District. Therefore the analysis excludes data for Kotido District.

The immediate objectives of the census were: - To create/update census field maps and lists of EAs for the control of the 2002 census and construction of efficient area sampling frames; - Effectively complete conducting a Population and Housing census with an Agricultural and Livestock module; - To generate basic demographic and socio-economic data from the 2002 census disaggregated by sex, age and administrative areas; - To compile agricultural and livestock sampling frames to be used in the subsequent sample surveys of these components; - To evaluate, analyze and disseminate the census results at all administrative levels.

Geographic coverage

The census covered the whole country.

Analysis unit

• Persons
• Households
• Communities

Universe

The census covered all the household members, all persons aged 5 years and above resident in the houseold, all persons aged 10 years and above resident in the household and all women aged 12 to 54 years resident in the household.

Kind of data

Census/enumeration data [cen]

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the 2002 Uganda Population and Housing Census were based on 1991 Census model with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, disability, religion, date of birth and orphanhood status. The household questionnaire also included the agricultural and micro and small enterprises modules. In addition to a household questionnaire, questionnaires were administered in each household for women aged 12-54, all persons aged 5 years and above, and also all persons aged 10 years and above.

Cleaning operations

Preliminary editing was carried out to identify, investigate and resolve inconsistencies resulting from possible data entry and / or coding errors. After completion of the preliminary editing, the edited data was subjected to the edit programmes in two phases. The first run was to undertake structural edits which in turn was ensuring that the entries were logical. The second run of the programme was aimed at ensuring completeness of content and as a result, missing values had to be imputed following logic embedded in the computer programs according to the editing specifications or rules established.

Data appraisal

A series of data quality tables are available to review the quality of the data and include the following: - Estimation of Population in an Area - Distribution of Households and Primary Sampling Units among strata - Age Tolerance limits used in matching individuals - Distribution of missing EAs during matching by strata - Distribution of EAs among strata - Un-weighted Number of matched and non-matched cases - Estimates of the Coverage rates - Population Estimates - Rate of Agreement by characteristics, residence and Region - Net Difference rate and Index of Inconsistency by characteristics - A list of PES indicators selected for computation of sampling errors - Reliability of Estimates Based on Selected Indicators at National Level - Reliability of Estimates Based on Selected Indicators For Urban Areas - Reliability of estimates Based on Selected indicators for rural Areas by Regions

The results of each of these data quality tables are shown in the appendix of the final report and are also given in the external resources section