This document was prepared to outline the methods used and citations for the calculation of the map layers considered to be the City of Vancouver's Equity Index, created by Kobel Solutions.

Indices are created by consolidating multidimensional data into a single representative measure known as an index, using a fundamental mathematical model. Most present indices are essentially the averages or weighted averages of the variables under study, ignoring multicollinearity among the variables, with the exception of the existing Ordinary Least Squares (OLS) estimator based OLS-PCA index methodology. Many existing surveys adopt survey designs that incorporate survey weights, aiming to obtain a representative sample of the population while minimizing costs. Survey weights play a crucial role in addressing the unequal probabilities of selection inherent in complex survey designs, ensuring accurate and representative estimates of population parameters. However, the existing OLS-PCA based index methodology is designed for simple random sampling and is incapable of incorporating survey weights, leading to biased estimates and erroneous rankings that can result in flawed inferences and conclusions for survey data. To address this limitation, we propose a novel Survey Weighted PCA (SW-PCA) based Index methodology, tailored for survey-weighted data. SW-PCA incorporates survey weights, facilitating the development of unbiased and efficient composite indices, improving the quality and validity of survey-based research. Simulation studies demonstrate that the SW-PCA based index outperforms the OLS-PCA based index that neglects survey weights, indicating its higher efficiency. To validate the methodology, we applied it to a Household Consumer Expenditure Survey (HCES), NSS 68th Round survey data to construct a Food Consumption Index for different states of India. The result was significant improvements in state rankings when survey weights were considered. In conclusion, this study highlights the crucial importance of incorporating survey weights in index construction from complex survey data. The SW-PCA based Index provides a valuable solution, enhancing the accuracy and reliability of survey-based research, ultimately contributing to more informed decision-making.

Context

Data and methodology for the Big Mac index https://www.economist.com/news/2018/07/11/the-big-mac-index.

Updated yearly, with data from 1986 to 2019.

Content

• big-mac-raw-index.csv contains values for the “raw” index
• big-mac-adjusted-index.csv contains values for the “adjusted” index
• big-mac-full-index.csv contains both

Acknowledgements

Banner Photo by amirali mirhashemian on Unsplash

This software is published by The Economist under the MIT licence. The data generated by The Economist are available under the Creative Commons Attribution 4.0 International License.

The licences include only the data and the software authored by The Economist, and do not cover any Economist content or third-party data or content made available using the software. More information about licensing, syndication and the copyright of Economist content can be found here.

From https://github.com/TheEconomist/big-mac-data

For more information regarding the index, please visit Standard & Poor's (https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-corelogic-cs-home-price-indices.pdf). There is more information about home price sales pairs in the Methodology section. Copyright, 2016, Standard & Poor's Financial Services LLC. Reprinted with permission.

Document provides information for the construction of the index, subindexes, and indicators of the Education for Democracy Index (EfDI)

US Census Annual Estimates of the Resident Population for Selected Age Groups by Sex for the United States. The estimates are based on the 2010 Census and reflect changes to the April 1, 2010 population due to the Count Question Resolution program and geographic program revisions. Median age is calculated based on single year of age. For population estimates methodology statements, see http://www.census.gov/popest/methodology/index.html.

The columns Return, TR, Long, and perTR list the annual returns in percentage, the number of transactions per year, the Long to Short position ratio, and the returns per transaction, respectively. The results are averaged over the entire test period.

The profit is summed over the entire test period (Jun. 5, 2006-Dec. 29, 2017). “Average” denotes the average profit.

This dataset package is focused on U.S construction materials and three construction companies: Cemex, Martin Marietta & Vulcan.

In this package, SpaceKnow tracks manufacturing and processing facilities for construction material products all over the US. By tracking these facilities, we are able to give you near-real-time data on spending on these materials, which helps to predict residential and commercial real estate construction and spending in the US.

The dataset includes 40 indices focused on asphalt, cement, concrete, and building materials in general. You can look forward to receiving country-level and regional data (activity in the North, East, West, and South of the country) and the aforementioned company data.

SpaceKnow uses satellite (SAR) data to capture activity and building material manufacturing and processing facilities in the US.

Data is updated daily, has an average lag of 4-6 days, and history back to 2017.

The insights provide you with level and change data for refineries, storage, manufacturing, logistics, and employee parking-based locations.

SpaceKnow offers 3 delivery options: CSV, API, and Insights Dashboard

Available Indices Companies: Cemex (CX): Construction Materials (covers all manufacturing facilities of the company in the US), Concrete, Cement (refinery and storage) indices, and aggregates Martin Marietta (MLM): Construction Materials (covers all manufacturing facilities of the company in the US), Concrete, Cement (refinery and storage) indices, and aggregates Vulcan (VMC): Construction Materials (covers all manufacturing facilities of the company in the US), Concrete, Cement (refinery and storage) indices, and aggregates

USA Indices:

Aggregates USA Asphalt USA Cement USA Cement Refinery USA Cement Storage USA Concrete USA Construction Materials USA Construction Mining USA Construction Parking Lots USA Construction Materials Transfer Hub US Cement - Midwest, Northeast, South, West Cement Refinery - Midwest, Northeast, South, West Cement Storage - Midwest, Northeast, South, West

Why get SpaceKnow's U.S Construction Materials Package?

Monitor Construction Market Trends: Near-real-time insights into the construction industry allow clients to understand and anticipate market trends better.

Track Companies Performance: Monitor the operational activities, such as the volume of sales

Assess Risk: Use satellite activity data to assess the risks associated with investing in the construction industry.

Index Methodology Summary Continuous Feed Index (CFI) is a daily aggregation of the area of metallic objects in square meters. There are two types of CFI indices; CFI-R index gives the data in levels. It shows how many square meters are covered by metallic objects (for example employee cars at a facility). CFI-S index gives the change in data. It shows how many square meters have changed within the locations between two consecutive satellite images.

How to interpret the data SpaceKnow indices can be compared with the related economic indicators or KPIs. If the economic indicator is in monthly terms, perform a 30-day rolling sum and pick the last day of the month to compare with the economic indicator. Each data point will reflect approximately the sum of the month. If the economic indicator is in quarterly terms, perform a 90-day rolling sum and pick the last day of the 90-day to compare with the economic indicator. Each data point will reflect approximately the sum of the quarter.

Where the data comes from SpaceKnow brings you the data edge by applying machine learning and AI algorithms to synthetic aperture radar and optical satellite imagery. The company’s infrastructure searches and downloads new imagery every day, and the computations of the data take place within less than 24 hours.

In contrast to traditional economic data, which are released in monthly and quarterly terms, SpaceKnow data is high-frequency and available daily. It is possible to observe the latest movements in the construction industry with just a 4-6 day lag, on average.

The construction materials data help you to estimate the performance of the construction sector and the business activity of the selected companies.

The foundation of delivering high-quality data is based on the success of defining each location to observe and extract the data. All locations are thoroughly researched and validated by an in-house team of annotators and data analysts.

See below how our Construction Materials index performs against the US Non-residential construction spending benchmark

Each individual location is precisely defined to avoid noise in the data, which may arise from traffic or changing vegetation due to seasonal reasons.

SpaceKnow uses radar imagery and its own unique algorithms, so the indices do not lose their significance in bad weather conditions such as rain or heavy clouds.

→ Reach out to get free trial

...

Ecuador Consumer Confidence Index: Machala data was reported at 37.521 Point in Jun 2019. This records an increase from the previous number of 37.290 Point for May 2019. Ecuador Consumer Confidence Index: Machala data is updated monthly, averaging 38.958 Point from Dec 2014 (Median) to Jun 2019, with 55 observations. The data reached an all-time high of 48.638 Point in Dec 2014 and a record low of 27.179 Point in May 2016. Ecuador Consumer Confidence Index: Machala data remains active status in CEIC and is reported by Central Bank of Ecuador. The data is categorized under Global Database’s Ecuador – Table EC.H005: Consumer Confidence Index: New Methodology.

United States RMI: Northeast: FM: Backlog of Remodeling Projects data was reported at 61.000 Point in Mar 2020. United States RMI: Northeast: FM: Backlog of Remodeling Projects data is updated quarterly, averaging 61.000 Point from Mar 2020 (Median) to Mar 2020, with 1 observations. United States RMI: Northeast: FM: Backlog of Remodeling Projects data remains active status in CEIC and is reported by National Association of Home Builders. The data is categorized under Global Database’s United States – Table US.EB067: Remodelling Market Index (New Methodology).

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information If you have questions about the underlying data stored here, please contact John Thomas, U.S. Environmental Protection Agency, at thomas.john@epa.gov. If you have questions about this metadata entry, please contact the CAFE team at climatecafe@bu.edu. "The National Walkability Index is a nationwide geographic data resource that ranks block groups according to their relative walkability. The national dataset includes walkability scores for all block groups as well as the underlying attributes that are used to rank the block groups. The National Walkability Index Methodology and User Guide (pdf) (2.63 MB, 2021) provides information on how to use the tool, as well as the methodology used to derive the index and ranked scores for its inputs. The index was developed using selected variables on density, diversity of land uses, and proximity to transit from the Smart Location Database. " [Quote from https://www.epa.gov/smartgrowth/national-walkability-index-user-guide-and-methodology]

For more information regarding the index, please visit Standard & Poor's (https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-corelogic-cs-home-price-indices.pdf). There is more information about home price sales pairs in the Methodology section. Copyright, 2016, Standard & Poor's Financial Services LLC. Reprinted with permission.

United States RMI: West: Market Outlook Compared to Three Months Ago data was reported at 60.000 Point in Jun 2020. This records an increase from the previous number of 28.000 Point for Mar 2020. United States RMI: West: Market Outlook Compared to Three Months Ago data is updated quarterly, averaging 44.000 Point from Mar 2020 (Median) to Jun 2020, with 2 observations. The data reached an all-time high of 60.000 Point in Jun 2020 and a record low of 28.000 Point in Mar 2020. United States RMI: West: Market Outlook Compared to Three Months Ago data remains active status in CEIC and is reported by National Association of Home Builders. The data is categorized under Global Database’s United States – Table US.EB067: Remodelling Market Index (New Methodology).

United States RMI: South: FM: Rate of Leads & Inquiries Coming In data was reported at 28.000 Point in Mar 2020. United States RMI: South: FM: Rate of Leads & Inquiries Coming In data is updated quarterly, averaging 28.000 Point from Mar 2020 (Median) to Mar 2020, with 1 observations. United States RMI: South: FM: Rate of Leads & Inquiries Coming In data remains active status in CEIC and is reported by National Association of Home Builders. The data is categorized under Global Database’s United States – Table US.EB067: Remodelling Market Index (New Methodology).

The notation of each target NN is listed in the first column. The shape of input xt and the shape of answer yt are listed in the last two columns, respectively. The shape (W) denotes a vector with a length of W, and the shape (W,W) denotes a W by W matrix.

United States RMI: Midwest: FM: Rate of Leads & Inquiries Coming In data was reported at 27.000 Point in Mar 2020. United States RMI: Midwest: FM: Rate of Leads & Inquiries Coming In data is updated quarterly, averaging 27.000 Point from Mar 2020 (Median) to Mar 2020, with 1 observations. United States RMI: Midwest: FM: Rate of Leads & Inquiries Coming In data remains active status in CEIC and is reported by National Association of Home Builders. The data is categorized under Global Database’s United States – Table US.EB067: Remodelling Market Index (New Methodology).

Overview: FEMA and Argonne National Laboratory completed the first analysis of community resilience indicators in 2018 and repeated the process in 2022. The analysis process begins with a literature review and cataloguing of published peer-reviewed assessment methodologies on social vulnerability and community resilience. The literature review findings are then filtered by inclusion criteria established by the research team to ensure the methodologies are:

Quantitative, Data and methodology are publicly available, Calculated at the county level or lower, Examine generalized hazard risk (rather than a singular hazard), and Focused on pre-disaster community conditions.

After this, the research team identifies the commonly used indicators across these methodologies and selects the best data source for each indicator. Finally, the research team bins the data for visualization, conducts a correlation analysis, and creates a composite index called the "FEMA Community Resilience Challenges Index (CRCI)".

In 2022, the FEMA and Argonne research team updated the 2018 literature review and examined 14 methodologies published between 2003 and 2021. Examining the indicators used in these methodologies, the research team identified 22 indicators as commonly used (indicators used in five or more of the 14 methodologies). The research team produced the FEMA CRCI at the county and the census tract levels. More details on these indicators and the research process can be found in the FEMA CRCI Storymap. Data last updated on May 13, 2023. This is the latest available version of the CRCI. Questions or comments about this layer? Email the RAPT team at FEMA-TARequest@fema.dhs.gov

For more information regarding the index, please visit Standard & Poor's (https://www.spglobal.com/spdji/en/documents/methodologies/methodology-sp-corelogic-cs-home-price-indices.pdf). There is more information about home price sales pairs in the Methodology section. Copyright, 2016, Standard & Poor's Financial Services LLC. Reprinted with permission.

The Transit Availability Index (formerly known as the Transit Accessibility Index) is an index measuring access to transit. The index is made up of 4 sub-indices: transit frequency, transit connectivity, sidewalk density, and transit proximity. The focus of the index is on examining how well the transit system as a whole serves the region. The index is not intended to reflect the actual transit service conditions one may encounter on a specific transit trip. It is also not intended as a means to evaluate the performance of the various transit operators nor is it a suitable tool for such an evaluation. For this analysis, transit service attributes are summed at the subzone-level geography for the seven-county region. Subzones are quarter-section sized geographies that CMAP uses for household and employment forecasting; generally they are ½ mile by ½ mile square throughout the region. Subzones in the Chicago Central Business District (CBD) are generally ¼ mile by ¼ mile square due to the densities of activities and the street network in that area.The original index was created in 2010 for the GO TO 2040 plan update. "In anticipation of the GO TO 2040 plan update, CMAP staff developed a new method of measuring access to transit as a means of determining the percentage of regional population and jobs with access to transit, one of the plan’s indicators for measuring the progress of plan implementation. This new method, the Transit Accessibility Index, severed as a uniform measure of transit level of service available during an average week. It permits us to track changes in transit level of service over time and present the results in an intuitive fashion. It also offers a universal comparison of the different service levels offered across the region. The inherent loss of some of the nuances in localized service is balanced against the ability of the index to provide a relatively simple way to compare transit service over a large area over time. This index also adheres to a number of tenets CMAP staff used in developing a revised set of performance measures for the GO TO 2040 plan update: principally that the indicator use actual observed data rather than modeled values, that it is widely comprehensible and that the data are updated with sufficient frequency for the index to serve as a reasonable access to transit index benchmark for measuring progress.”The 2023 update of transit availability is a continuation of these efforts using 2019 data. One notable change is that the Pedestrian Environment Factor is no longer used as a measurement of transit availability. Rather, we have replaced this sub-index with sidewalk density. Another notable change is that the 2017 subzones were used for this analysis, whereas previous iterations used the 2009 subzones. As such, it is not suggested to directly compare the new transit availability index with previous versions of transit availability/accessibility.Transit Availability Index, Indicator Methodology Excerpt