Population:😮

• A population is a distinct group of individuals, whether that group comprises a nation or a group of people with a common characteristic. In statistics, a population is the pool of individuals from which a statistical sample is drawn for a study. What is the types of population?
• And while every population pyramid is unique, most can be categorized into three prototypical shapes: expansive (young and growing), constrictive (elderly and shrinking), and stationary (little or no population growth). Let's take a deeper dive into the trends these three shapes reveal about a population and its needs.
• The global growth rate in absolute numbers accelerated to a peak of 92.9 million in 1988, but has declined to 81.3 million in 2020. Long-term projections indicate that the growth rate of the human population of this planet will continue to decline, and that by the end of the 21st century, it will reach zero.

1- Temporal fluctuations in growth rates can arise from both variation in age-specific vital rates and temporal fluctuations in age structure (i.e., the relative abundance of individuals in each age-class). However, empirical assessments of temporal fluctuations in age structure and their effects on population growth rate are rare. Most research has focused on understanding the contribution of changing vital rates to population growth rates and these analyses routinely assume that: (i) populations have stable age distributions, (ii) environmental influences on vital rates and age structure are stationary (i.e., the mean and/or variance of these processes does not change over time), and (iii) dynamics are independent of density. 2- Here we quantified fluctuations in age structure and assessed whether they were stationary for four populations of free-ranging vertebrates: moose (observed for 48 years), elk (15 years), tawny owls (15 years) and gray wolves (17 years). We also assessed the extent that fluctuations in age structure were useful for predicting annual population growth rates using models which account for density-dependence. 3- Fluctuations in age structure were of a similar magnitude to fluctuations in abundance. For three populations (moose, elk, owls), the mean and the skew of the age distribution fluctuated without stabilizing over the observed time periods. More precisely, the sample variance (interannual variance) of age structure indices increased with the length of the study period which suggests that fluctuations in age structure were non-stationary for these populations – at least over the 15-48 year periods analysed. 4- Fluctuations in age structure were associated with population growth rate for two populations. In particular, population growth varied from positive to negative for moose and from near zero to negative for elk as the average age of adults increased over its observed range. 5- Non-stationarity in age structure may represent an important mechanism by which abundance becomes non-stationary – and therefore difficult to forecast – over time scales of concern to wildlife managers. Overall, our results emphasize the need for vertebrate populations to be modelled using approaches that consider transient dynamics and density-dependence, and that do not rely on the assumption that environmental processes are stationary.

Abstract

Over the past decade, Albania has been seeking to develop the framework for a market economy and more open society. It has faced severe internal and external challenges in the interim - extremely low income levels and a lack of basic infrastructure, the rapid collapse of output and inflation rise after the shift in regime in 1991, the turmoil during the 1997 pyramid crisis, and the social and economic shocks accompanying the 1999 Kosovo crisis. In the face of these challenges, Albania has made notable progress in creating conditions conducive to growth and poverty reduction. A poverty profile based on 1996 data (the most recent available) showed that some 30 percent of the rural and some 15 percent of the urban population are poor, with many others vulnerable to poverty due to their incomes being close to the poverty threshold. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood. To date, the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Multi-purpose household surveys are one of the main sources of information to determine living conditions and measure the poverty situation of a country and provide an indispensable tool to assist policymakers in monitoring and targeting social programs. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) - the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS) - drew attention, once again, to the need for accurately measuring household welfare according to well accepted standards, and for monitoring these trends on a regular basis. In spite of their narrow scope and limitations, these two surveys have provided the country with an invaluable training ground towards the development of a permanent household survey system to support the government strategic planning in its fight against poverty. In the process leading to its first Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyse on a regular basis the information it needs to inform policy-making. In its first phase (2001-2006), this monitoring system will include the following data collection instruments:

(i) Population and Housing Census (ii) Living Standards Measurement Surveys every 3 years (iii) annual panel surveys.

The Population and Housing Census (PHC) conducted in April 2001, provided the country with a much needed updated sampling frame which is one of the building blocks for the household survey structure. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (in 2003, 2004 and 2006), drawing heavily on the 2001 census information. The possibility to include a panel component in the second LSMS will be considered at a later stage, based on the experience accumulated with the first panels. The 2002 LSMS was in the field between April and early July, with some field activities (the community and price questionnaires) extending into August and September. The survey work was undertaken by the Living Standards unit of INSTAT, with the technical assistance of the World Bank. The present document provides detailed information on this survey. Section II summarizes the content of the survey instruments used. Section III focuses on the details of the sample design. Sections IV describes the pilot test and fieldwork procedures of the survey, as well as the training received by survey staff. Section V reviews data entry and data cleaning issues. Finally, section VI contains a series of annotations that all those interested in using the data should read.

Geographic coverage

National

Analysis unit

Households

Kind of data

Sample survey data [ssd]

Sampling procedure

(a) SAMPLING FRAME

The Republic of Albania is divided geographically into 12 Prefectures (Prefekturat). The latter are divided into Districts (Rrethet) which are, in turn, divided into Cities (Qyteti) and Communes (Komunat). The Communes contain all the rural villages and the very small cities. For the April 2001 General Census of Population and Housing census purposes, the cities and the villages were divided into Enumeration Areas (EAs). These formed the basis for the LSMS sampling frame. The EAs in the frame are classified by Prefecture, District, City or Commune. The frame also contains, for every EA, the number of Housing Units (HUs), the number of occupied HUs, the number of unoccupied HUs, and the number of households. Occupied dwellings rather than total number of dwellings were used since many census EAs contain a large number of empty dwellings. The Housing Unit (defined as the space occupied by one household) was taken as the sampling unit, instead of the household, because the HU is more permanent and easier to identify in the field. A detailed review of the list of censuses EAs shows that many have zero population. In order to obtain EAs with a minimum of 50 and a maximum of 120 occupied housing units, the EAs with zero population were first removed from the sampling frame. Then, the smallest EAs (with less than 50 HU) were collapsed with geographically adjacent ones and the largest EAs (with more than 120 HU) were split into two or more EAs. Subsequently, maps identifying the boundaries of every split and collapsed EA were prepared Sample Size and Implementation Since the 2002 LSMS had been conducted about a year after the April 2001 census, a listing operation to update the sample EAs was not conducted. However, given the rapid speed at which new constructions and demolitions of buildings take place in the city of Tirana and its suburbs, a quick count of the 75 sample EAs was carried out followed by a listing operation. The listing sheets prepared during the listing operation became the sampling frame for the final stage of selection. The final sample design for the 2002 LSMS included 450 Primary Sampling Units (PSUs) and 8 households in each PSU, for a total of 3600 households. Four reserve units were selected in each sample PSU to act as replacement unit in non-response cases. In a few cases in which the rate of migration was particularly high and more than four of the originally selected households could not be found for the interview, additional households for the same PSU were randomly selected. During the implementation of the survey there was a problem with the management of the questionnaires for a household that had initially refused, but later accepted, to fill in the food diary. The original household questionnaire was lost in the process and it was not possible to match the diary with a valid household questionnaire. The household had therefore to be dropped from the sample (this happened in Shkoder, PSU 16). The final sample size is therefore of 3599 households.

(b) STRATIFICATION

The sampling frame was divided in four regions (strata), Coastal Area, Central Area, and Mountain Area, and Tirana (urban and other urban). These four strata were further divided into major cities, other urban, and other rural. The EAs were selected proportionately to the number of housing units in these areas. In the city of Tirana and its suburbs, implicit stratification was used to improve the efficiency of the sample design. The implicit stratification was performed by ordering the EAs in the sampling frame in a geographic serpentine fashion within each stratum used for the independent selection of EAs.

Mode of data collection

Face-to-face [f2f]

Cleaning operations

(a) QUALITY CHECKS Besides the checks built-in in the DE program and those performed on the preliminary versions of the dataset as it was building up, and additional round of in depth checks on the household questionnaire and the food diary was performed in late September and early October in Tirana. Wherever possible data entry errors or inconsistencies in the dataset were spotted, the original questionnaires or diary were retrieved, and the information contained therein checked. Changes were made to the August version of the dataset as needed and the dataset was finalized in October.

(b) DATA ENTRY Data Entry Operations Data entry for all the survey instruments was performed using custom made applications developed in CS-Pro. Data entry for the household questionnaire was performed in a decentralized fashion in parallel with the enumeration, so as to allow for 'real-time' checking of the data collected. This allowed a further tier of quality control checks on the data. Where errors in the data were spotted during data entry, it was possible to instruct enumerators and supervisors to correct the information, if necessary, revisiting the household, when the teams were still in the field. A further round of checks was performed by the core team in Tirana and Bank staff in Washington as the data were gathered from the field and the entire dataset started building up. All but one of the 16 teams in the districts had one DEO, the Fier team had two, and there were four DEO's for Tirana. Each DEO worked with a laptop computer, and was given office space in the regional Statistics Offices, or in INSTAT headquarters for the Tirana teams. The DEO's received Part 1 of the household questionnaire from the supervisor once the supervisor had checked the enumerator's work, within two

Statistical Area 2 2023 update

SA2 2023 is the first major update of the geography since it was first created in 2018. The update is to ensure SA2s are relevant and meet criteria before each five-yearly population and dwelling census. SA2 2023 contains 135 new SA2s. Updates were made to reflect real world change of population and dwelling growth mainly in urban areas, and to make some improvements to their delineation of communities of interest.

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Description

This dataset is the definitive version of the annually released statistical area 2 (SA2) boundaries as at 1 January 2023 as defined by Stats NZ. This version contains 2,395 SA2s (2,379 digitised and 16 with empty or null geometries (non-digitised)).

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SA2 is an output geography that provides higher aggregations of population data than can be provided at the statistical area 1 (SA1) level. The SA2 geography aims to reflect communities that interact together socially and economically. In populated areas, SA2s generally contain similar sized populations.

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The SA2 should:

form a contiguous cluster of one or more SA1s,

excluding exceptions below, allow the release of multivariate statistics with minimal data suppression,

capture a similar type of area, such as a high-density urban area, farmland, wilderness area, and water area,

be socially homogeneous and capture a community of interest. It may have, for example:

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• a shared road network,
• shared community facilities,
• shared historical or social links, or
• socio-economic similarity,

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form a nested hierarchy with statistical output geographies and administrative boundaries. It must:

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• be built from SA1s,
• either define or aggregate to define SA3s, urban areas, territorial authorities, and regional councils.

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SA2s in city council areas generally have a population of 2,000–4,000 residents while SA2s in district council areas generally have a population of 1,000–3,000 residents.

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In major urban areas, an SA2 or a group of SA2s often approximates a single suburb. In rural areas, rural settlements are included in their respective SA2 with the surrounding rural area.

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SA2s in urban areas where there is significant business and industrial activity, for example ports, airports, industrial, commercial, and retail areas, often have fewer than 1,000 residents. These SA2s are useful for analysing business demographics, labour markets, and commuting patterns.

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In rural areas, some SA2s have fewer than 1,000 residents because they are in conservation areas or contain sparse populations that cover a large area.

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To minimise suppression of population data, small islands with zero or low populations close to the mainland, and marinas are generally included in their adjacent land-based SA2.

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Zero or nominal population SA2s

To ensure that the SA2 geography covers all of New Zealand and aligns with New Zealand’s topography and local government boundaries, some SA2s have zero or nominal populations. These include:

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• SA2s where territorial authority boundaries straddle regional council boundaries. These SA2s each have fewer than 200 residents and are: Arahiwi, Tiroa, Rangataiki, Kaimanawa, Taharua, Te More, Ngamatea, Whangamomona, and Mara.
• SA2s created for single islands or groups of islands that are some distance from the mainland or to separate large unpopulated islands from urban areas
• SA2s that represent inland water, inlets or oceanic areas including: inland lakes larger than 50 square kilometres, harbours larger than 40 square kilometres, major ports, other non-contiguous inlets and harbours defined by territorial authority, and contiguous oceanic areas defined by regional council.
• SA2s for non-digitised oceanic areas, offshore oil rigs, islands, and the Ross Dependency. Each SA2 is represented by a single meshblock. The following 16 SA2s are held in non-digitised form (SA2 code; SA2 name):

400001; New Zealand Economic Zone, 400002; Oceanic Kermadec Islands, 400003; Kermadec Islands, 400004; Oceanic Oil Rig Taranaki, 400005; Oceanic Campbell Island, 400006; Campbell Island, 400007; Oceanic Oil Rig Southland, 400008; Oceanic Auckland Islands, 400009; Auckland Islands, 400010 ; Oceanic Bounty Islands, 400011; Bounty Islands, 400012; Oceanic Snares Islands, 400013; Snares Islands, 400014; Oceanic Antipodes Islands, 400015; Antipodes Islands, 400016; Ross Dependency.

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SA2 numbering and naming

Each SA2 is a single geographic entity with a name and a numeric code. The name refers to a geographic feature or a recognised place name or suburb. In some instances where place names are the same or very similar, the SA2s are differentiated by their territorial authority name, for example, Gladstone (Carterton District) and Gladstone (Invercargill City).

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SA2 codes have six digits. North Island SA2 codes start with a 1 or 2, South Island SA2 codes start with a 3 and non-digitised SA2 codes start with a 4. They are numbered approximately north to south within their respective territorial authorities. To ensure the north–south code pattern is maintained, the SA2 codes were given 00 for the last two digits when the geography was created in 2018. When SA2 names or boundaries change only the last two digits of the code will change.

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For more information please refer to the Statistical standard for geographic areas 2023.

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Generalised version

This generalised version has been simplified for rapid drawing and is designed for thematic or web mapping purposes.

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Macrons

Names are provided with and without tohutō/macrons. The column name for those without macrons is suffixed ‘ascii’.

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Digital data

Digital boundary data became freely available on 1 July 2007.

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To download geographic classifications in table formats such as CSV please use Ariā