This dataset provides the average (annual, winter, summer) residential metered water consumption within residential neighbourhoods provided in m3/month for the City of Edmonton.

Average monthly residential winter water consumption is the average consumption of the following months: January, February, March, April, October, November and December.

Average monthly residential summer water consumption is the average consumption of the following months: May, June, July, August and September.

Only those residential neighbourhoods with at least ten accounts are illustrated to ensure customer privacy.

Residential consumption refers to water used primarily for domestic purposes, where no more than four separate dwelling units are metered by a single water meter.

Thematic mapping is based on the following ranges:

0-10 m3/month – orange 10-20 m3/month – green 20-30 m3/month – purple 30-35 m3/month – blue 35-60 m3/month – red 60 m3/month and up – maroon

Potable water use by sector and average daily use for Canada, provinces and territories.

This dataset provides the average (annual, winter, summer) residential metered water consumption (2016) within residential neighbourhoods provided in m3/month for the City of Edmonton. Average monthly residential winter water consumption is the average consumption of the following months: January, February, March, April, October, November and December. Average monthly residential summer water consumption is the average consumption of the following months: May, June, July, August and September.

Only those residential neighbourhoods with at least ten accounts are illustrated to ensure customer privacy.

Residential consumption refers to water used primarily for domestic purposes, where no more than four separate dwelling units are metered by a single water meter.

Thematic mapping is based on the following ranges:

0-10 m3/month – orange 10-20 m3/month – green 20-30 m3/month – purple 30-35 m3/month – blue 35-60 m3/month – red 60 m3/month and up – maroon

There is no description for this dataset.

Data History

Data Origin

Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.

Data Triage Considerations

This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.

Identification of Critical Infrastructure

This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.

Commercial Risks and Anonymisation Individual Identification Risks

There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.

Meter and Property Association

Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.

Interpretation of Null Consumption

Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.

Meter Re-reads

The dataset must account for instances where meters are read multiple times for accuracy.

Joint Supplies & Multiple Meters per Household

Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.

Schema Consistency with the Energy Industry

In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.

After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.

Schema The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.

Aggregation to Mitigate Risks The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.

Data Freshness Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data. Publish Frequency Weekly.

Data Triage Review Frequency An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.

Data Specifications For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include: • Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption. • Where it is necessary to estimate consumption, this is calculated based on actual meter readings. • Meters of all types (smart, dumb, AMR) are included in this dataset. • The dataset is updated and published Weekly. • Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release. • The dataset includes LSOAs with 2 or more meters. Any LSOAs with less than 2 meters have been excluded. • Consumption data is only included where we have the full consumption data for a year for a given meter.

Context Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.

The geographical data provided does not pinpoint locations of water meters within an LSOA.

The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Supplementary InformationBelow is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.1.Ofwat guidance on water meters. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf Data Schema DATA_SOURCE: Company that provided the data YEAR: The calendar year covered by the data LSOA_CODE: LSOA or Data Zone converted code of the meter location NUMBER_OF_METERS: Number of meters within an LSOA TOTAL_CONSUMPTION: Average consumption within the LSOA TOTAL_CONSUMPTION_UNITS: Units for average consumption

China Water Consumption: City: Daily per Capita: Residential data was reported at 188.799 l in 2023. This records an increase from the previous number of 184.732 l for 2022. China Water Consumption: City: Daily per Capita: Residential data is updated yearly, averaging 178.638 l from Dec 1978 (Median) to 2023, with 46 observations. The data reached an all-time high of 220.240 l in 2000 and a record low of 120.600 l in 1978. China Water Consumption: City: Daily per Capita: Residential data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCA: Water Consumption: Daily per Capita: Residential.

This data release contains the input-data files and R scripts associated with the analysis presented in [citation of manuscript]. The spatial extent of the data is the contiguous U.S. The input-data files include one comma separated value (csv) file of county-level data, and one csv file of city-level data. The county-level csv (“county_data.csv”) contains data for 3,109 counties. This data includes two measures of water use, descriptive information about each county, three grouping variables (climate region, urban class, and economic dependency), and contains 18 explanatory variables: proportion of population growth from 2000-2010, fraction of withdrawals from surface water, average daily water yield, mean annual maximum temperature from 1970-2010, 2005-2010 maximum temperature departure from the 40-year maximum, mean annual precipitation from 1970-2010, 2005-2010 mean precipitation departure from the 40-year mean, Gini income disparity index, percent of county population with at least some college education, Cook Partisan Voting Index, housing density, median household income, average number of people per household, median age of structures, percent of renters, percent of single family homes, percent apartments, and a numeric version of urban class. The city-level csv (city_data.csv) contains data for 83 cities. This data includes descriptive information for each city, water-use measures, one grouping variable (climate region), and 6 explanatory variables: type of water bill (increasing block rate, decreasing block rate, or uniform), average price of water bill, number of requirement-oriented water conservation policies, number of rebate-oriented water conservation policies, aridity index, and regional price parity. The R scripts construct fixed-effects and Bayesian Hierarchical regression models. The primary difference between these models relates to how they handle possible clustering in the observations that define unique water-use settings. Fixed-effects models address possible clustering in one of two ways. In a "fully pooled" fixed-effects model, any clustering by group is ignored, and a single, fixed estimate of the coefficient for each covariate is developed using all of the observations. Conversely, in an unpooled fixed-effects model, separate coefficient estimates are developed only using the observations in each group. A hierarchical model provides a compromise between these two extremes. Hierarchical models extend single-level regression to data with a nested structure, whereby the model parameters vary at different levels in the model, including a lower level that describes the actual data and an upper level that influences the values taken by parameters in the lower level. The county-level models were compared using the Watanabe-Akaike information criterion (WAIC) which is derived from the log pointwise predictive density of the models and can be shown to approximate out-of-sample predictive performance. All script files are intended to be used with R statistical software (R Core Team (2017). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org) and Stan probabilistic modeling software (Stan Development Team. 2017. RStan: the R interface to Stan. R package version 2.16.2. http://mc-stan.org).

Kolkata had the highest average water consumption per person across major Indian cities in 2021, at 2.31 liters per day. Bhubaneshwar followed, with an average consumption of 2.3 liters per day. The recommended amount of water intake to stay hydrated is at least two liters every day.

This dataset provides the following customer data by year for Edmonton’s residential, multi-residential and commercial customer classes:

a. Total metered water consumption in megalitres (ML) i.e. million litres b. Average number of monthly active services c. Average monthly water use (m3/active service/month)

The total metered water consumption (ML) for the regional customer class is also provided.

The following definitions apply:

A residential customer uses water primarily for domestic purposes, where no more than four separate dwelling units are metered by a single water meter.

A multi-residential customer uses water primarily for domestic purposes, where more than four separate dwelling units are metered by a single water meter.

A commercial customer includes all commercial, industrial and institutional users within the city of Edmonton.

A regional customer is a customer outside the city of Edmonton who is supplied water through a water supply agreement.

A family in Flanders consists of an average of 2.3 people in 2022. This average family has an average tap water consumption of 70 m3 per year or 84 liters per person per day.

​Monthly residential water consumption grouped by zip code and customer class.

A family in Flanders consists of an average of 2.3 people in 2022. This average family has an average tap water consumption of 70 m3 per year or 84 liters per person per day.

Data History

Data Origin

Domestic consumption data is recorded using water meters. The consumption recorded is then sent back to water companies. This dataset is extracted from the water companies.

Data Triage Considerations

This section discusses the careful handling of data to maintain anonymity and addresses the challenges associated with data updates, such as identifying household changes or meter replacements.

Identification of Critical Infrastructure

This aspect is not applicable for the dataset, as the focus is on domestic water consumption and does not contain any information that reveals critical infrastructure details.

Commercial Risks and Anonymisation Individual Identification Risks

There is a potential risk of identifying individuals or households if the consumption data is updated irregularly (e.g., every 6 months) and an out-of-cycle update occurs (e.g., after 2 months), which could signal a change in occupancy or ownership. Such patterns need careful handling to avoid accidental exposure of sensitive information.

Meter and Property Association

Challenges arise in maintaining historical data integrity when meters are replaced but the property remains the same. Ensuring continuity in the data without revealing personal information is crucial.

Interpretation of Null Consumption

Instances of null consumption could be misunderstood as a lack of water use, whereas they might simply indicate missing data. Distinguishing between these scenarios is vital to prevent misleading conclusions.

Meter Re-reads

The dataset must account for instances where meters are read multiple times for accuracy.

Joint Supplies & Multiple Meters per Household

Special consideration is required for households with multiple meters as well as multiple households that share a meter as this could complicate data aggregation.

Schema Consistency with the Energy Industry

In formulating the schema for the domestic water consumption dataset, careful consideration was given to the potential risks to individual privacy. This evaluation included examining the frequency of data updates, the handling of property and meter associations, interpretations of null consumption, meter re-reads, joint suppliers, and the presence of multiple meters within a single household as described above.

After a thorough assessment of these factors and their implications for individual privacy, it was decided to align the dataset's schema with the standards established within the energy industry. This decision was influenced by the energy sector's experience and established practices in managing similar risks associated with smart meters. This ensures a high level of data integrity and privacy protection.

Schema The dataset schema is aligned with those used in the energy industry, which has encountered similar challenges with smart meters. However, it is important to note that the energy industry has a much higher density of meter distribution, especially smart meters.

Aggregation to Mitigate Risks The dataset employs an elevated level of data aggregation to minimise the risk of individual identification. This approach is crucial in maintaining the utility of the dataset while ensuring individual privacy. The aggregation level is carefully chosen to remove identifiable risks without excluding valuable data, thus balancing data utility with privacy concerns.

Data Freshness Users should be aware that this dataset reflects historical consumption patterns and does not represent real-time data. Publish Frequency Weekly.

Data Triage Review Frequency An annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.

Data Specifications For the domestic water consumption dataset, the data specifications are designed to ensure comprehensiveness and relevance, while maintaining clarity and focus. The specifications for this dataset include: • Each dataset encompasses recordings of domestic water consumption as measured and reported by the data publisher. It excludes commercial consumption. • Where it is necessary to estimate consumption, this is calculated based on actual meter readings. • Meters of all types (smart, dumb, AMR) are included in this dataset. • The dataset is updated and published Weekly. • Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release. • The dataset includes LSOAs with 2 or more meters. Any LSOAs with less than 2 meters have been excluded. • Consumption data is only included where we have the full consumption data for a year for a given meter.

Context Users are cautioned against using the dataset for immediate operational decisions regarding water supply management. The data should be interpreted considering potential seasonal and weather-related influences on water consumption patterns.

The geographical data provided does not pinpoint locations of water meters within an LSOA.

The dataset aims to cover a broad spectrum of households, from single-meter homes to those with multiple meters, to accurately reflect the diversity of water use within an LSOA.

Supplementary InformationBelow is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.1.Ofwat guidance on water meters. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf Data Schema DATA_SOURCE: Company that provided the data YEAR: The calendar year covered by the data LSOA_CODE: LSOA or Data Zone converted code of the meter location NUMBER_OF_METERS: Number of meters within an LSOA TOTAL_CONSUMPTION: Average consumption within the LSOA TOTAL_CONSUMPTION_UNITS: Units for average consumption

iUTAH researchers contacted municipal water provider organizations in the 12 cities represented in the 2014 household survey that maintain billing records or other water use records. Of the 12, 11 cities released data under a strict confidentiality agreement outlined in a memorandum of understanding to link water bills from months in 2014 to parcels or buildings where individual survey respondents were located. The water bills were matched to results from a 2014 household survey. Researchers at Utah State University and the University of Utah implemented the ‘2014 iUTAH Household Survey’ with over 2,300 randomly selected households in 2014 in 23 neighborhoods in 12 Utah communities. The survey included detailed individual- and household-level information about water management behaviors, perceptions of water resource conditions, and attitudes toward a range of water policies and programs.

The survey research team leaders agreed to: • Treat any water use or billing records with care and discretion and to respect the privacy rights of individual water system customers. • Aggregate the results of our analysis so that the historic water use levels and water bills of any individual customer, building or parcel are not released in any publicly accessible document, presentation, or report. • Never share the detailed water use records with any other individual or group without the expressed written permission of the municipal water provider organization. • Ensure that any person who has access to the raw individual-level survey and water use datasets have completed institutional review board human subjects research training, are currently certified and authorized to work with the data, and agree to the stringent confidentiality protocols listed above. • Not reveal the specific location or identity of individual respondents to the 2014 iUTAH Household Survey to any other individual or organization, including the partner municipal water provider organization.

The municipal water provider organization representatives agreed to: • Provide an electronic dataset of billing or water use records that permit a reliable estimate of actual rates of water consumption at the parcel or building scale. • Address, tax parcel, or other information that allows these records to be linked to the individual parcels, buildings, or customer addresses. • Not require the research team to reveal to the municipal provider the identity of which specific parcels or households were either sampled into or responded to our survey.

The data cleaning process included the following steps: a. Calculate monthly estimates b. Calculate per capita based on household size c. Calculate per acreage d. Calculate tiered cost e. Match household survey responses with water bill data

The map shows total municipal needs by province and territory. Domestic water consumption includes the quantity of water used for household purposes such as washing, food preparation, and bathing. Across Canada, nearly all of the water used by municipal water systems comes from lakes and rivers the remainder (12% of the total) comes from groundwater. Establishing and maintaining water systems is costly. There are three major costs: water supply, infrastructure maintenance, and administration. Water prices across Canada are generally low compared to other countries. Monthly bills range between $15 and $90, the lowest being in Quebec, Newfoundland, and British Columbia, and the highest in the Prairie Provinces and northern Canada. Although water usage rates vary across Canada, the overall per capita use is very high compared to that of other industrialized countries. Only the United States has higher rates of municipal water usage.

This dataset provides the average (annual, winter, summer) residential metered water consumption (by year) within 400 m x 400m hexagons (approximately two city blocks) provided in m3/month for the City of Edmonton.

Average monthly residential winter water consumption is the average consumption of the following months: January, February, March, April, October, November and December.

Average monthly residential summer water consumption is the average consumption of the following months: May, June, July, August and September.

Only those hexagons that contain at least ten accounts are illustrated to ensure customer privacy.

Residential consumption refers to water used primarily for domestic purposes, where no more than four separate dwelling units are metered by a single water meter.

Thematic mapping is based on the following ranges:

0-10 m3/month – orange 10-20 m3/month – green 20-30 m3/month – purple 30-35 m3/month – blue 35-60 m3/month – red 60 m3/month and up – maroon

Water Consumption: City: Daily per Capita: Residential: Beijing data was reported at 167.264 l in 2023. This records an increase from the previous number of 163.221 l for 2022. Water Consumption: City: Daily per Capita: Residential: Beijing data is updated yearly, averaging 187.520 l from Dec 1995 (Median) to 2023, with 29 observations. The data reached an all-time high of 281.840 l in 1998 and a record low of 152.910 l in 2005. Water Consumption: City: Daily per Capita: Residential: Beijing data remains active status in CEIC and is reported by Ministry of Housing and Urban-Rural Development. The data is categorized under China Premium Database’s Utility Sector – Table CN.RCA: Water Consumption: Daily per Capita: Residential.

This dataset demonstrates the affordability of the average Austin Water residential customer’s annual combined water and wastewater bill as a percentage of median household income. Austin Water utilized CensusReporter.org for 2019 and 2020 MHI data. The American Community Survey is the source for Census Reporter. Data sources: Austin Water Rates and Charges Team and American Community Survey (ACS) reported by the U.S. Census Bureau, DataUSA, and CensusReporter.org. View more details and insights related to this dataset on the story page: https://data.austintexas.gov/stories/s/Percent-of-median-household-income-spent-on-the-av/w8c4-v9a2

The dataset contains year- and region-wise compiled data on all india distribution (per thousand) of households by distance travelled, average time taken and types of persons engaged in fetching drinking water. The different categories of data contained in the types of persons fetching drinking water include male and female of age 18 years and above, hired labour and others, and the distances travelled to fetch drinking water include less than 0.2, 0.2 to 0.5, 0.5 to 1, 1 to 1.5 and more kilometers

About this dataset

Context

The dataset presents the mean household income for each of the five quintiles in Sweet Water, AL, as reported by the U.S. Census Bureau. The dataset highlights the variation in mean household income across quintiles, offering valuable insights into income distribution and inequality.

Key observations

• Income disparities: The mean income of the lowest quintile (20% of households with the lowest income) is 13,037, while the mean income for the highest quintile (20% of households with the highest income) is 171,539. This indicates that the top earners earn 13 times compared to the lowest earners.
• *Top 5%: * The mean household income for the wealthiest population (top 5%) is 236,325, which is 137.77% higher compared to the highest quintile, and 1812.73% higher compared to the lowest quintile.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Income Levels:

• Lowest Quintile
• Second Quintile
• Third Quintile
• Fourth Quintile
• Highest Quintile
• Top 5 Percent

Variables / Data Columns

• Income Level: This column showcases the income levels (As mentioned above).
• Mean Household Income: Mean household income, in 2022 inflation-adjusted dollars for the specific income level.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Sweet Water median household income. You can refer the same here