A table showing standardized coefficients of the structural equation model for HWB indices.

This file is the result of an overlay operation between the land take for the 2019 state at 10x10 m2 resolution and the destinations according to the ‘space accounting file’, state 01/01/2020, surprised to a resolution of 10x10 m2. The concept of ‘land take’ is defined in the White Paper Space Policy Plan as that part of the space in which the biophysical function is no longer the most important. In other words, it is about the space occupied by our settlements (i.e. for housing, industrial and commercial purposes, transport infrastructure, recreational purposes and also parks and gardens). This definition is based on the European Commission’s definition of ‘settlement area’ or ‘artificial land’, namely ‘the area of land used for housing, industrial and commercial purposes, health care, education, nursing infrastructure, roads and rail networks, recreation (parks and sports grounds), etc. In land use planning, it usually corresponds to all land uses beyond agriculture, semi-natural areas, forestry, and water bodies’ (EC, 2012). The budget for space, which was included in the Spatial Structure Plan Flanders (RSV), sets the quantitative targets for the increases and decreases in the areas of the various categories of use. The so-called space accounting (SAB) of the RSV is the monitoring tool used to calculate the follow-up of these targets with regard to these (planological) zoning categories. This is a monitoring of planned land use, therefore the RBH does not reflect information on actual land use. The different destinations on the plans of construction and spatial implementation plans at regional, provincial and municipal level have been taken into account according to a number of destination categories: housing, industry, industry within the gates, recreation, nature and reserve, forest, other greenery, agriculture and other destinations. For the purpose of the annual calculation of the ‘Space accounting RSV’ indicator, a GIS file was built up, which is updated with the current state of the relevant information from the relevant zoning plans in each calculation. The analysis and discussion in this indicator is based on this interim work file. In the following, we refer to this GIS file with the term ‘space accounting’. For the calculation of this composite indicator, the destinations were divided into ‘soft destinations’ (i.e. intended not to be dominated by land take) and ‘hard destinations’ (i.e. intended to be dominated by land take) on the basis of their RBH categories. The destinations belonging to the RBH categories ‘Other green’ and ‘Other’ are assigned according to a table that appears with the detailed description of this indicator in the technical report referred to above. The land take for the 2019 condition at 10x10 m2 resolution is compared in this indicator with the destinations according to the space accounting file, condition 01/01/2020, surprised to a resolution of 10x10 m2. From the confrontation between space take and space accounting, four categories can be deduced: • Cat A: ‘hard intended’ with land take • Cat B: ‘soft’ intended with land take • Cat C: ‘hard’ intended without land take • Cat D: ‘soft’ intended without land take The area figures resulting from the land take overlay (accurate to 0.01 hectares) and the space accounting file shall be rounded to an accuracy level of 10ha. More details about the creation of this product and the corresponding figures are therefore now referred to the technical report 'Indicators Spatial Efficiency, condition and evolution 2013-2019 - technical data sheets' that you can find under https://archief-algemeen.omgeving.vlaanderen.be/xmlui/handle/acd/762878

Conceptual framework of the index system of human well-being (HWB).

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Weighted Mean Vegetation Health Index (Mean VHI).If the index is below 40, different levels of vegetation stress, losses of crop and pasture production might be expected; if the index is above 60 (favorable condition) plentiful production might be expected. VHI is very useful for an advanced prediction of crop losses.

Dit bestand is het resultaat van een overlay-bewerking tussen het ruimtebeslag voor de toestand 2013 op 10x10 m² resolutie en de bestemmingen volgens het ‘ruimteboekhoudingsbestand’, toestand 01/01/2014, verrasterd naar een resolutie van 10x10 m². Het concept ‘ruimtebeslag’ is gedefinieerd in het Witboek Beleidsplan Ruimte als dat deel van de ruimte waarin de biofysische functie niet langer de belangrijkste is. Het gaat, met andere woorden, over de ruimte die ingenomen worden door onze nederzettingen (dus voor huisvesting, industriële en commerciële doeleinden, transportinfrastructuur, recreatieve doeleinden en ook parken en tuinen). Deze definitie is gebaseerd op de definitie die de Europese Commissie hanteert voor ‘settlement area’ of ‘artificial land’, namelijk "the area of land used for housing, industrial and commercial purposes, health care, education, nursing infrastructure, roads and rail networks, recreation (parks and sports grounds), etc. In land use planning, it usually corresponds to all land uses beyond agriculture, semi-natural areas, forestry, and water bodies." (EC, 2012). De begroting van de ruimte, die in het Ruimtelijk Structuurplan Vlaanderen (RSV) werd opgenomen, legt de kwantitatieve streefcijfers vast van de toe- en afnamen van de oppervlakten van de verschillende bestemmingscategorieën. De zogenaamde ruimteboekhouding (RBH) van het RSV is het monitoringsinstrument waarmee de opvolging van deze streefcijfers met betrekking tot deze (planologische) bestemmingscategorieën berekend wordt. Het gaat om een monitoring van gepland landgebruik, bijgevolg geeft de RBH geen informatie over het feitelijke ruimtegebruik weer. De verschillende bestemmingen op de plannen van aanleg en ruimtelijke uitvoeringsplannen op gewestelijk, provinciaal en gemeentelijk niveau zijn daarin verrekend volgens een aantal bestemmingscategorieën: wonen, industrie, industrie binnen de poorten, recreatie, natuur en reservaat, bos, overig groen, landbouw en overige bestemmingen. Met het oog op de jaarlijks herhaalde berekening van de indicator ‘ruimteboekhouding RSV’ werd een GIS-bestand opgebouwd dat bij elke berekening wordt geactualiseerd met de op dat moment actuele toestand van de relevante informatie uit de betrokken bestemmingsplannen. De analyse en bespreking in deze indicator is gebaseerd op dit tussentijds werkbestand. In wat volgt verwijzen we naar dit GIS-bestand met de term ‘ruimteboekhoudingsbestand’. Voor de berekening van deze samengestelde indicator werden de bestemmingen opgedeeld in ‘zachte bestemmingen’ (i.e. bestemd om niet gedomineerd te worden door ruimtebeslag) en ‘harde bestemmingen’ (i.e. bestemd om gedomineerd te worden door ruimtebeslag) op basis van hun RBH-categorieën. De bestemmingen behorende tot de RBH-categorieën ‘Overig groen’ en ‘Overige’ worden daarbij toegekend volgens een tabel die verschijnt bij de gedetailleerde beschrijving van deze indicator in het verder vermelde technisch rapport. Het ruimtebeslag voor de toestand 2013 op 10x10 m² resolutie wordt in deze indicator vergeleken met de bestemmingen volgens het ruimteboekhoudingsbestand, toestand 01/01/2014, verrasterd naar een resolutie van 10x10 m². Uit de confrontatie tussen het ruimtebeslag en de ruimteboekhouding kunnen 4 categorieën afgeleid worden: • Cat A: ‘hard bestemd’ met ruimtebeslag • Cat B: ‘zacht’ bestemd met ruimtebeslag • Cat C: ‘hard’ bestemd zonder ruimtebeslag • Cat D: ‘zacht’ bestemd zonder ruimtebeslag De oppervlaktecijfers die resulteren uit de overlay van het ruimtebeslag (nauwkeurig tot op 0.01 hectare) en het ruimteboekhoudingsbestand moeten afgerond worden op een nauwkeurigheidsniveau van 10ha. Deze geodatalaag werd in 2021 vervangen door een herwerkte versie voor toestand 2013, om conform te blijven met de bijgestelde methode die gehanteerd werd bij de opmaak van toestand 2019. Meer details over de totstandkoming van dit product en de bijhorende cijfers wordt daarom nu verwezen naar het technisch rapport 'Indicatoren Ruimtelijk Rendement, toestand en evolutie 2013-2019 - technische fiches' dat je terugvindt onder https://archief-algemeen.omgeving.vlaanderen.be/xmlui/handle/acd/762878

(1) Gender Inequality Index (GII) is compiled by the United Nations Development Programme (UNDP) to measure gender inequality in the areas of reproductive health, empowerment, and labor market. Our country calculates the index based on the UNDP formula.(2) Explanation: (1) GII is used to measure the difference in development achievements between the two genders, with a value between 0 and 1, where a smaller value is better. (2) Due to our country's non-membership in the United Nations and unique international situation, the index is calculated by our department according to the UNDP formula, incorporating our country's data. The calculation of the composite index for each year mainly uses the data year of various indicators adopted by UNDP. (3) In order to have the same standard for international comparison, the composite index and rankings, once published, will not be retrospectively adjusted.(3) Notes: (1) In 2011, UNDP adjusted the formula for the maternal mortality ratio in the Human Development Report, resulting in a significant decrease in GII values for each country, and the data for retrospective adjustments will not be re-ranked. (2) The original indicator "Labor force participation rate for ages 15-64" has been changed to "Labor force participation rate for ages 15 and above"; UNDP has not released the global GII ranking for 2016.

(1) Gender Gap Index (GGI) is compiled by the World Economic Forum (WEF) and consists of four sub-indices: "economic participation and opportunity," "educational attainment," "health and survival," and "political empowerment." It measures the gender differences in the allocation of social resources and opportunities. Our country calculates it according to the WEF formula. (2) Explanation: (1) GGI measures the gender equality gap, with a value between 0 and 1, where higher values are better. (2) Our country's data is calculated by the central authority according to the WEF formula. The calculation of the composite index for each year is based on the main data from the WEF's indicators. WEF sets equal standards for the female-to-male ratio for each indicator, with the exception of health life expectancy for females to males (1.06) and the sex ratio at birth (0.944), which are used as the baseline. Ratios exceeding the equal standard are replaced by the equal standard value. (3) In order to have the same benchmark for international comparison, the composite index and rankings will not be retroactively adjusted after their publication. (4) Since April 23, 2019, it is compiled by the Gender Equality Department of the Executive Yuan and can be found at the website https://www.gender.ey.gov.tw/gecdb/Stat_International_Node0.aspx?stZ7cAGjLH7DDUmC9hAf%2f4g%3d%3d.

This dataset contains the statewide composite patient safety and Adverse Events indicator (PSI) rate used to determine the “Incidence of measurable hospital-acquired conditions” rate for the Let’s Get Healthy California Initiative. PSI rates may not be comparable across years as significant changes were made to composition, definition, and calculation of PSI over time. The current composite PSI includes the following component indicators: pressure ulcer, iatrogenic pneumothorax, in-hospital fall-associated fracture, postoperative hemorrhage or hematoma, postoperative acute kidney injury requiring dialysis, postoperative respiratory failure, perioperative pulmonary embolism or deep vein thrombosis, postoperative sepsis, postoperative wound dehiscence, abdominopelvic accidental puncture or laceration.

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Mean Vegetation Health Index (Mean VHI). VHI combines both the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). The TCI is calculated using a similar equation to the VCI, but relates the current temperature to the long-term maximum and minimum , as it is assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A decrease in the VHI would, for example, indicate relatively poor vegetation conditions and warmer temperatures, signifying stressed vegetation conditions, and over a longer period would be indicative of drought. In ASIS, VHI is computed in two modality: dekadal and monthly. The dekadal/monthly VHI raster layer published in Hand in Hand Geospatial platform is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.). Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=background More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]

Based on the asymmetric copula function, this paper analyzes the static and dynamic correlation between Shanghai Composite Index and Shenzhen Composite Index. Through the static analysis, it is found that the asymmetric copula function is better than Gumbel Copula in describing the distribution characteristics of the top tail dependence between the Shanghai Composite Index and the Shenzhen Composite Index, and the copula correlation coefficient definition based on the asymmetric copula function can well describe the asymmetric dependence between variables. In the time-varying analysis, the paper improves the traditional dynamic evolution equation of the tail-dependence coefficient. Through empirical analysis, the result shows that the improved dynamic evolution equation can better reflect the dynamic evolution process of the tail-dependence coefficient.

A table showing descriptive statistics of HWB indices from 2006 to 2012.

Equation for calculating λ', a composite index of population performance based on r', which estimates the realized per capita rate of population change for each replicate cohort.

Abstract

Comparison of economic performance over time is a key factor in economic analysis and a fundamental requirement for policy-making. Short-term indicators play an important role in this context by providing such comparison indicators. Among the short-term indicators, the Index of Industrial Production (IIP) has historically been one of the most well-known and well-used indicators. The all India IIP is a composite indicator that measures the short-term changes in the volume of production of a basket of industrial products during a given period with respect to that in a chosen base period. It is compiled and published monthly by the Central Statistics Office (CSO) with a time lag of six weeks from the reference month.

Geographic coverage

Industrial Coverage: Although UNSD guidelines state that IIP is to be compiled for activities in ISIC Rev. 4 Sections B, C, D and E, i.e. (i) Mining and quarrying, (ii) Manufacturing, (iii) Electricity, Gas, Steam and Air-conditioning supply and (iv) Water supply, Sewerage, Waste management and Remediation activities, due to constraints of the data availability and other resources, the index is being compiled with (i) Mining, (ii) Manufacturing and (iii) Electricity as scope of All India IIP. In the current base year (i.e. 2011-12), the index covers 839 items clubbed into 407 item groups under three sectors i.e. Mining (29 items clubbed into 1 item group), Manufacturing (809 items clubbed into 405 item groups) and Electricity (1 item) with weights of 14.37%, 77.63% and 7.99% respectively.

The mining sector covers 29 items under different headings viz. Fuel Minerals, Metallic Minerals and Non-Metallic Minerals. This sector also includes Crude Petroleum, Natural Gas, Coal and Lignite. The manufacturing sector covers 809 items under different groups e.g. Food products, Beverages, Textiles, Chemicals and chemical products etc. The Electricity sector is treated as a single item.

Product Coverage: Within an industry the products are covered on the basis of the concepts of Primary (Main) Product as well as Secondary (By) Product. All those items which represent at least 80% of the output within each industry group, i.e., 3-digit industry of NIC-2008 (based on ISIC 4) have been included in the Item basket. Essential products like tea, coffee, salt and sugar have been included. The over-riding criteria for finalization of item basket have been the regular monthly flow of production data from the source agencies/collection authorities.

Analysis unit

Frame for coverage of units is decided by the source agencies which collect data from the factories. For compilation of IIP both large and medium factories are covered for collection of data by the source agencies.

Mode of data collection

The sample size for data collection is decided by the source agencies. Generally, efforts are made to cover all the major units.

statistical techniques :

Procedures for Non-Response: In India, the Index of Industrial Production is based on the responded production as well as estimated production for non-responding units. The production estimates for the non-responding units are developed using various methods including: repetition of last available data; taking the average production data for the last few months; using previous year's growth rate; etc. The appropriate estimation procedure is decided by the source agencies themselves in consultation with CSO. Treatment of Missing Production: The index is compiled on the basis of the data on a fixed number of items collected from the source agencies which in turn collect the data from different factories and estimate the data on their own, as per the requirements. Selection of Replacement Items: Replacement of items is not done at present. Introducing New Units and Products: New units/ new products are included only at the time of the revision of base year.

Other statistical procedures : The production figures, if not reported by all the units in the current month due to any reason, are estimated for the current month and revised subsequently in the next month, and finally in the third month on the basis of which the final indices for a month are calculated.

Nature of Weights: The weights for the three sectors (mining, manufacturing, and electricity) are based on share of the sector in total domestic production in the base year. The overall weight of the manufacturing sector is apportioned to the industry groups at the 2-digit, 3-digit- and 4-digit level of the National Industrial Classification (NIC) 2008, on the basis of the Gross Value Added (GVA). The weighting diagram for the current series of IIP is prepared on the basis of GVA up to the 2-digit, 3 and 4 digit level of NIC based on the results of ASI 2011- 12. At the final level (i.e. 5 digit level of NIC), weights to items have been distributed on the basis of Gross Value of Output (GVO). The weights of selected items within an industry group are apportioned on the basis of the value of output.

Period of Current Index Weights: The current index weights are based on the value of production of the industries during the base year period viz. April, 2011 to March 2012 as reported in the Annual Survey of Industries for the year 2011-12. The same weights are used until the revision of the base year is done.

Frequency of Weight Updates: The weights are revised with every revision of the base year. The base year was revised to 2011-12 from 2004-05 in May 2017. Efforts would be made to revise the base year once in every five years as per UNSD's recommendations (the previous base years of the index were 2004-05, 1993-94, 1980-81, 1970, 1956, 1951 and 1946).

• Computation of lowest level indices: The lowest level, for which an index is prepared, is the item group. It is compiled as the ratio of production quantity in the current month with respect to its average monthly production quantity in the base year.

• Aggregation: The IIP is calculated using the Laspeyres formula as a weighted arithmetic average of production relatives. The index is primarily quantity based, although for some item groups the quantity relatives are obtained by price deflation.

The index at group level/ 2-digit level of NIC is compiled by using the Laspeyeres' formula, i.e. I = Uppercase sigma(Wi*Ri)/ Uppercase sigm(Wi) where Ri is the production relative and Wi is the weight of an item.

The index is prepared for each two-digit level of NIC. Also the index is prepared on the basis of the following use-based classification: Primary Goods, Capital Goods, Intermediate Goods, Infrastructure/ Construction Goods, Durable Consumer Goods and Non-Durable Consumer Goods.

• Alignment of Value of Weights and Base Period: No alignment of the weights is required as the weights as well as the base year production relate to the same reference period viz. April, 2011 to March 2012.

-- Linking of Re-weighted Index to Historical Index: Whenever there is change in the base year, the new series can be linked with the old series by preparing linked series. For the common period, the index series are available with both old weights & new weights for linking the two series.

The data provide event study market model calculation for 621 firms from a population of 634 firms to assess Indonesia equity market reaction on earnings announcement information content by using book-to-market as the proxy. The data include calculation of CAR, CAAR, Book-to-market value, stock prices, composite index, et cetera.

The Vegetation Health Index (VHI) illustrates the severity of drought based on the vegetation health and the influence of temperature on plant conditions. The VHI is a composite index and the elementary indicator used to compute the seasonal drought indicators in ASIS: Agricultural Stress Index (ASI), Drought Intensity and Mean Vegetation Health Index (Mean VHI). VHI combines both the Vegetation Condition Index (VCI) and the Temperature Condition Index (TCI). The TCI is calculated using a similar equation to the VCI, but relates the current temperature to the long-term maximum and minimum , as it is assumed that higher temperatures tend to cause a deterioration in vegetation conditions. A decrease in the VHI would, for example, indicate relatively poor vegetation conditions and warmer temperatures, signifying stressed vegetation conditions, and over a longer period would be indicative of drought. In ASIS, VHI is computed in two modality: dekadal and monthly. The dekadal/monthly VHI raster layer published in Hand in Hand Geospatial platform is further updated in the following 5 dekads (improve data precision, remove cloud pixel etc.). Flags of raster file: 251=missing, 252=cloud, 253=snow, 254=sea, 255=background More information, please visit FAO GIEWS Earth Observation website: https://www.fao.org/giews/earthobservation/index.jsp?lang=en Data license policy: Creative Commons Attribution- NonCommercial-ShareAlike 3.0 IGO (CC BY-NC- SA 3.0 IGO) Recommended citation: © FAO - Agricultural Stress Index System (ASIS), http://www.fao.org/giews/earthobservation/, [Date accessed]

Dimensions, indicators, cut-offs and weights of women’s multidimensional empowerment index (MEI), BDHS, 2017–18.

In this work responpents are employee from microfinance shariah industries in Pekalongan and Banyumas, Indonesia. The tool used to analyze this study is structural equation modelling by AMOS program. This data consist of factor loading, indicator data, and data composite.

This file is the result of an overlay operation between the land take for the 2019 state at 10x10 m2 resolution and the destinations according to the ‘space accounting file’, state 01/01/2020, surprised to a resolution of 10x10 m2. The concept of ‘land take’ is defined in the White Paper Space Policy Plan as that part of the space in which the biophysical function is no longer the most important. In other words, it is about the space occupied by our settlements (i.e. for housing, industrial and commercial purposes, transport infrastructure, recreational purposes and also parks and gardens). This definition is based on the European Commission’s definition of ‘settlement area’ or ‘artificial land’, namely ‘the area of land used for housing, industrial and commercial purposes, health care, education, nursing infrastructure, roads and rail networks, recreation (parks and sports grounds), etc. In land use planning, it usually corresponds to all land uses beyond agriculture, semi-natural areas, forestry, and water bodies’ (EC, 2012). The budget for space, which was included in the Spatial Structure Plan Flanders (RSV), sets the quantitative targets for the increases and decreases in the areas of the various categories of use. The so-called space accounting (SAB) of the RSV is the monitoring tool used to calculate the follow-up of these targets with regard to these (planological) zoning categories. This is a monitoring of planned land use, therefore the RBH does not reflect information on actual land use. The different destinations on the plans of construction and spatial implementation plans at regional, provincial and municipal level have been taken into account according to a number of destination categories: housing, industry, industry within the gates, recreation, nature and reserve, forest, other greenery, agriculture and other destinations. For the purpose of the annual calculation of the ‘Space accounting RSV’ indicator, a GIS file was built up, which is updated with the current state of the relevant information from the relevant zoning plans in each calculation. The analysis and discussion in this indicator is based on this interim work file. In the following, we refer to this GIS file with the term ‘space accounting’. For the calculation of this composite indicator, the destinations were divided into ‘soft destinations’ (i.e. intended not to be dominated by land take) and ‘hard destinations’ (i.e. intended to be dominated by land take) on the basis of their RBH categories. The destinations belonging to the RBH categories ‘Other green’ and ‘Other’ are assigned according to a table that appears with the detailed description of this indicator in the technical report referred to above. The land take for the 2019 condition at 10x10 m2 resolution is compared in this indicator with the destinations according to the space accounting file, condition 01/01/2020, surprised to a resolution of 10x10 m2. From the confrontation between space take and space accounting, four categories can be deduced: • Cat A: ‘hard intended’ with land take • Cat B: ‘soft’ intended with land take • Cat C: ‘hard’ intended without land take • Cat D: ‘soft’ intended without land take The area figures resulting from the land take overlay (accurate to 0.01 hectares) and the space accounting file shall be rounded to an accuracy level of 10ha. More details about the creation of this product and the corresponding figures are therefore now referred to the technical report 'Indicators Spatial Efficiency, condition and evolution 2013-2019 - technical data sheets' that you can find under https://archief-algemeen.omgeving.vlaanderen.be/xmlui/handle/acd/762878

Mediation results obtained from structural equation modeling using BDHS, 2017–18 data.

Percentage distribution of covariates and prevalence of good ENC for home births in Bangladesh.