In recent years, utility companies in several provinces have started installing wireless smart meters in Canadian businesses and residences. Some people have expressed concern about the possibility of health effects from exposure to the radiofrequency fields that these devices emit.

Overview    This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level. Key Definitions    Aggregation   The process of summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.     AMR Meter Automatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from a water meter remotely and periodically. Dataset   Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.  Data Zone Data zones are the key geography for the dissemination of small area statistics in Scotland Dumb Meter A dumb meter or analogue meter is read manually. It does not have any external connectivity. Granularity   Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours   ID   Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.    LSOA Lower Layer Super Output Areas (LSOA) are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales. Open Data Triage   The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.    Schema   Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.    Smart Meter A smart meter is an electronic device that records information and communicates it to the consumer and the supplier. It differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier. Units   Standard measurements used to quantify and compare different physical quantities.  Water Meter Water metering is the practice of measuring water use. Water meters measure the volume of water used by residential and commercial building units that are supplied with water by a public water supply system. 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  Annually 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 annually. Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release. 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.

Overview

This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level.

Key Definitions

Aggregation

The process of summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes.

AMR Meter

Automatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from a water meter remotely and periodically.

Dataset

Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.

Data Zone

Data zones are the key geography for the dissemination of small area statistics in Scotland

Dumb Meter

A dumb meter or analogue meter is read manually. It does not have any external connectivity.

Granularity

Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours

ID

Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance.

LSOA

Lower Layer Super Output Areas (LSOA) are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales.

Open Data Triage

The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data.

Schema

Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute.

Smart Meter

A smart meter is an electronic device that records information and communicates it to the consumer and the supplier. It differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier.

Units

Standard measurements used to quantify and compare different physical quantities.

Water Meter

Water metering is the practice of measuring water use. Water meters measure the volume of water used by residential and commercial building units that are supplied with water by a public water supply system.

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

Annually

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 annually.

·
Historical data may be made available to facilitate trend analysis and comparative studies, although it is not mandatory for each dataset release.

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 Information

1. Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset.

2. Ofwat guidance on water meters

3. https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf

Data HistoryData OriginDomestic 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 ConsiderationsThis 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 InfrastructureThis 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.Individual Identification RisksThere 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 AssociationChallenges 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 ConsumptionInstances 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-readsThe dataset must account for instances where meters are read multiple times for accuracy.Joint Supplies & Multiple Meters per HouseholdSpecial 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.SchemaThe 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 RisksThe 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 FreshnessUsers should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.Publish FrequencyAnnuallyData Triage Review FrequencyAn annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.Data SpecificationsFor 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.Consumption for calendar year dates 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 annually.​The dataset includes LSOAs with 10 or more meters. Any LSOAs with less than 10 meters have been excluded.​The dataset includes only meters that are currently shown as active.​The dataset excludes any meters where consumption is recorded as null.ContextUsers 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.​This dataset has been calculated using actual read data. To align reads to calendar year, our approach uses a combination of previous and next usage readings, along with the number of days between these readings to calculate the total consumption.Supplementary Information​Below is a curated selection of links for additional reading, which provide a deeper understanding of this dataset:Ofwat guidance on water metersData SchemaDATA_SOURCE: Company that provided the dataYEAR: The calendar year covered by the dataLSOA_CODE: LSOA or Data Zone converted code of the meter locationNUMBER_OF_METERS: Number of meters within an LSOATOTAL_CONSUMPTION: Total consumption within the LSOATOTAL_CONSUMPTION_UNITS: Units for total consumption

In recent years, utility companies in several provinces have started installing wireless smart meters in Canadian businesses and residences. Some people have expressed concern about the possibility of health effects from exposure to the radiofrequency fields that these devices emit.

OverviewThis dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level.Key DefinitionsAggregation The process of summarising or grouping data to obtain a single or reduced set of information, often for analysis or reporting purposes. AMR MeterAutomatic meter reading (AMR) is the technology of automatically collecting consumption, diagnostic, and status data from a water meter remotely and periodically.Dataset Structured and organised collection of related elements, often stored digitally, used for analysis and interpretation in various fields.Data ZoneData zones are the key geography for the dissemination of small area statistics in ScotlandDumb MeterA dumb meter or analogue meter is read manually. It does not have any external connectivity.Granularity Data granularity is a measure of the level of detail in a data structure. In time-series data, for example, the granularity of measurement might be based on intervals of years, months, weeks, days, or hours.ID Abbreviation for Identification that refers to any means of verifying the unique identifier assigned to each asset for the purposes of tracking, management, and maintenance. LSOALower Layer Super Output Areas (LSOA) are a geographic hierarchy designed to improve the reporting of small area statistics in England and Wales.Open Data Triage The process carried out by a Data Custodian to determine if there is any evidence of sensitivities associated with Data Assets, their associated Metadata and Software Scripts used to process Data Assets if they are used as Open Data. Schema Structure for organising and handling data within a dataset, defining the attributes, their data types, and the relationships between different entities. It acts as a framework that ensures data integrity and consistency by specifying permissible data types and constraints for each attribute. Smart MeterA smart meter is an electronic device that records information and communicates it to the consumer and the supplier. It differs from automatic meter reading (AMR) in that it enables two-way communication between the meter and the supplier.Units Standard measurements used to quantify and compare different physical quantities.Water MeterWater metering is the practice of measuring water use. Water meters measure the volume of water used by residential and commercial building units that are supplied with water by a public water supply system.Data HistoryData 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 ConsiderationsThis 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 InfrastructureThis 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 AnonymisationIndividual Identification RisksThere 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 AssociationChallenges 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 ConsumptionInstances 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 IndustryIn 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.SchemaThe 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 RisksThe 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 Triage Review FrequencyAn annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.Data FreshnessUsers should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.Publish FrequencyAnnuallyData SpecificationsFor 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 annually.• 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.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, or a single meter for multiple domestic units, to accurately reflect the diversity of water use within an LSOA.This dataset has been aggregated from actual read data and does not use estimated values to align reads to calendar years. Our approach subtracts the latest meter read from the preceding year from the latest meter read in the reported year; this is divided by the number of days between the two reads to obtain an average daily consumption. Data are removed from meters considered as void for seven or more months during the year. Void properties are those within the company’s supply area, which are connected for either a water service only, a wastewater service only or both services but do not receive a charge, as there are no occupants.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.pdf2. Wessex Water performance commitment data on void sites: https://marketplace.wessexwater.co.uk/dataset/void-sites-performance-commitment-data

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

This dataset offers valuable insights into yearly domestic water consumption across various Lower Super Output Areas (LSOAs) or Data Zones, accompanied by the count of water meters within each area. It is instrumental for analysing residential water use patterns, facilitating water conservation efforts, and guiding infrastructure development and policy making at a localised level.Data HistoryData OriginDomestic 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 ConsiderationsThis 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 InfrastructureThis 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 AnonymisationIndividual Identification RisksThere 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 AssociationChallenges 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 ConsumptionInstances 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-readsThe dataset must account for instances where meters are read multiple times for accuracy.Joint Supplies & Multiple Meters per HouseholdSpecial 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 IndustryIn 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.SchemaThe 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 RisksThe 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 FreshnessUsers should be aware that this dataset reflects historical consumption patterns and does not represent real-time data.Publish FrequencyAnnually (1st of April to the 31st of March)Data Triage Review FrequencyAn annual review is conducted to ensure the dataset's relevance and accuracy, with adjustments made based on specific requests or evolving data trends.Data SpecificationsFor 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 annually (1st of April to the 31st of March).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 10 or more meters. Any LSOAs with less than 10 meters have been excluded.ContextUsers 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:Stream - The Value of a Domestic Consumption Data Use Case: https://www.streamwaterdata.co.uk/apps/0742154c5c1547cb8df235ccf2f66d57/exploreOfwat - Guidance on water meters: https://www.ofwat.gov.uk/wp-content/uploads/2015/11/prs_lft_101117meters.pdf

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

This dataset contains IoT-based data designed to track and analyze the carbon footprint of individuals, based on various factors such as energy consumption, transportation activity, and environmental conditions. The dataset includes 10,000 entries, each representing an individual, with the following features:

Person_ID: A unique identifier for each individual (from 1 to 10,000). Energy_Usage_kWh: The total daily energy consumption in kilowatt-hours, tracked via smart meters. Values range from 2 to 50 kWh. Transportation_Distance_km: The total daily distance traveled by the individual in kilometers, tracked via GPS. Values range from 0 to 100 km. Vehicle_Type: The mode of transportation used by the individual, with possible values: "Car", "Bus", "Walking", and "Electric Vehicle." (Note: "Bike" has been excluded). Smart_Appliance_Usage_hours: The daily usage (in hours) of smart appliances within the home, ranging from 1 to 12 hours. Renewable_Energy_Usage_percent: The percentage of energy usage that comes from renewable sources, ranging from 0% to 100%. Building_Type: The type of building where the individual resides, with possible values: "Residential" and "Commercial." Temperature_C: The ambient temperature in Celsius, tracked by smart thermostats or weather stations, ranging from -10°C to 40°C. Humidity_percent: The percentage of humidity, tracked by IoT humidity sensors, with values ranging from 20% to 90%. Carbon_Emission_kgCO2: The estimated carbon emissions (in kilograms of CO₂) resulting from the individual's energy usage and transportation activities

Overview

This dataset is designed to analyze the relationship between cyber attacks, geographic locations, and Internet of Things (IoT) device types. The data has been collected from multiple sources, including cybersecurity incident reports, infrastructure data, environmental conditions, and transportation networks. With a total of 65,450 records, this dataset provides valuable insights for cybersecurity research, smart cities, and artificial intelligence applications.

Dataset Columns and Their Descriptions 1. ID This is a unique identifier assigned to each record in the dataset. It is stored as an integer and serves as the primary key for tracking individual entries.

1. Latitude This column represents the latitude coordinate of the geographic location where the cyber attack or IoT device activity occurred. It is stored as a floating-point number, with values ranging from -90 to 90.

2. Longitude Similar to latitude, this column stores the longitude coordinate of the location. It is also a floating-point number, with values ranging from -180 to 180.

3. Location Type This field describes the type of location where the cyber attack or IoT device was recorded. It is stored as a categorical string and includes values such as "Railway," "Gas Station," "Hospital," "City Boundary," and "River".

4. Elevation (m) This column contains the elevation of the recorded location, measured in meters above sea level. It is a floating-point number, typically ranging from 0 to 5000 meters.

5. Population Density (people/km²) This field provides the population density of the given location, measured in people per square kilometer. It is stored as an integer, with values ranging from 50 to 10,000.

6. Temperature (°C) This column records the temperature at the time of the cyber attack or IoT device activity, measured in degrees Celsius. It is stored as a floating-point number, with values between -30 and 50°C.

7. Humidity (%) This field represents the relative humidity of the location at the time of the event. It is stored as a floating-point number, with values ranging from 10% to 100%.

8. Rainfall (mm) This column captures the amount of rainfall in the location at the given time, measured in millimeters. It is stored as a floating-point number, with values ranging from 0 to 300 mm.

9. Infrastructure Type This field indicates the type of infrastructure present at the location of the event. It is stored as a categorical string and includes values like "Bridge," "Sewage System," "Dam," and "Power Line."

10. Air Quality Index (AQI) This column records the Air Quality Index (AQI) at the time of the attack or device activity. It is stored as an integer, with values ranging from 0 (clean air) to 500 (hazardous air quality).

11. Traffic Flow (vehicles/hour) This field provides the number of vehicles passing through the location per hour. It is stored as an integer, typically ranging from 100 to 5000 vehicles per hour.

12. Public Transport Station This column describes the nearest public transport station to the event location. It is stored as a categorical string and includes values such as "Bus Stop," "Metro Station," and "None."

13. Cyber Attack Type This field identifies the type of cyber attack recorded at the given location. It is stored as a categorical string and includes values like "Phishing," "DDoS," "Malware," "Zero-Day Exploit," and "SQL Injection."

14. IoT Device Category This column categorizes the type of IoT device involved in the event. It is stored as a categorical string and includes categories like "Smart Home," "Industrial IoT," "Smart City," "Wearable," and "Healthcare IoT."

15. IoT Device Type This field provides a more detailed classification of the specific IoT device within the assigned category. It is stored as a categorical string and includes values such as "Smartwatch," "Security Camera," "IoT Sensor," "Smart Meter," and "Remote Patient Monitor."

This dataset is structured to enable comprehensive cybersecurity, geospatial, and AI-driven analyses, making it valuable for research in cyber attack prevention, IoT security, and smart city planning.

Dataset Contents The dataset includes geographic information such as latitude, longitude, and elevation, which help identify the most targeted areas for cyber attacks. It also contains population data, allowing for an analysis of how population density influences cyber threats. Environmental factors like temperature, humidity, and rainfall are included, providing insights into the impact of weather conditions on IoT security.

Additionally, the dataset contains infrastructure-related data such as the type of facilities present at the attack locations, including bridges, sewage systems, and power lines. It also includes information on air quality (AQI index) and traffic flow data, helping analyze how congestion levels might be linked to cyber threats...

This repository contains raw datasets gathered under the EPSRC-funded Humanitarian Engineering and Energy for Displacement (HEED) research project (EP/P029531/1). The project aimed to understand energy needs of displaced communities by creating an evidence base on the usage of seven different energy interventions, and provide recommendations for improved design of future energy interventions to better meet the needs of people. Below is a brief description of the interventions.

1. Stove-use monitoring systems (July 2019 to October 2019) - Stove-use monitoring systems (SUMs) were deployed on clay stoves in Kigeme camp, Rwanda in July 2019. The aim of the study was to evaluate stove usage patterns by measuring temperature profiles within stove enclosure and on the surface of stoves. The SUMs consisted of 2 sensors - a thermocouple to measure temperature within the stove and a Si7021 sensor to measure temperature outside the stove, connected to an Arduino MKR GSM 1400 board. The data measured by the sensors was stored only if the change in values exceeded a set threshold for either of the readings. The SUMs were powered by a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.
   
   The study was conducted in 2 phases. In phase 1 (02 July 2019 to 30 September 2019), data was collected from 15 SUMs and stored locally on SD cards as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (02 October 2019 to 17 October 2019), data was collected from 9 SUMs and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. The data from both phases of study is deposited here in SUM.zip. The cleaned dataset is available at 10.5281/zenodo.3946999.
   
2. Mobile Lantern monitoring systems (July 2019 to December 2019) - Mobile lantern monitoring systems (LMSs) were deployed in Nyabiheke camp, Rwanda, in July 2019. The aim of the study was to evaluate lantern usage (static or mobile) and consumption (charge and discharge) patterns. The monitors consisted of a D.light S30 solar lantern fitted with an Arduino-based monitoring device. The most integral part of the device was the Arduino MKR GSM 1400 board connected to an ADXL345 inertial motion unit sensor. The ADXL was used to calculate step count of a user based on activity and freefall interrupts. Additionally, the voltage of lantern battery was measured using an in-house voltage monitor to understand the discharging and charging patterns. The step count and battery voltage data were stored only if a significant change in the step count was detected. The LMSs were powered through a re-chargeable Li-Ion battery of 3.7V and a rating of 7.59Wh.
   
   The study was conducted in 2 phases. In phase 1 (03 July 2019 to 30 September 2019), data was collected from 60 lanterns and stored locally on SD card as well as communicated to a remote server via GSM. The time of data collection was recorded using GSM functionality. However, several GSM and MQTT failures were noted leading to loss of timestamp values as well as shorter battery lifetime due to re-transmission tries. In phase 2 (09 October 2019 to 18 December 2019), the design of lantern monitors was modified to circumvent these issues. The data was collected from 54 lanterns data and only stored locally on SD cards. The time of data collection was recorded using an external RTC clock connected to the Arduino board. Additionally, an internal watchdog timer was used to reset the device in case of failures. While certain failures persisted, the data yield was considerably higher than phase 1 of the study. The data from both phases of study is deposited here in LMS.zip. The cleaned dataset is available at 10.5281/zenodo.4269809.
   
3. Individual appliance monitors (December 2018 to January 2020) - Individual appliance monitors (IAMs) were deployed in Uttargaya settlement, Nepal, in December 2018. The IAMs were simple, cost-effective and unobtrusive devices to collect data on the energy usage of connected appliances. The aim of the study was to understand energy consumption and usage patterns of different household appliances in grid-connected sub-metered displaced communities. The monitoring system consisted of 2 types of devices – Energenie MiHome Smart Plugs MIHO005 (referred to as the IAM) to sense data relating to power and voltage drawn by the connected appliance, and gateway nodes to collect data from IAM. The main component of the gateway node was a Raspberry Pi fitted with an Energenie ENER314-RT (receiver-transmitter) add-on board to allow the Pi to communicate with the smart plugs. The data collected by the RPi gateway was stored locally in an SD card as well as sent to a remote server hosted at Coventry University.
   
   The study was conducted until January 2020. The raw data from the study is deposited here in IAM.zip. The cleaned dataset is available at 10.5281/zenodo.4271714.
   
4. Footfall monitoring systems (December 2018 to January 2020) - Seven footfall monitoring systems (FMSs) were deployed alongside seven solar streetlights to measure step count of passers-by in the Uttargaya settlement, Nepal, in December 2018. The aim of the study was to understand the level of pedestrian movement in the area and evaluate the effect of streetlights on the level of activity. Therefore, the footfall monitors were deployed prior to commissioning of streetlights to gather baseline data. The footfall monitors consisted of a Raspberry Pi 3B, PiFace Real Time Clock and CAM008 70º night vision IR sensor to detect footfall. Upon detection, footfall count along with the direction of movement and the timestamp (measured from PiFace RTC) was stored onto an SD card and communicated to a remote server hosted at Coventry University.
   
   The study was conducted until January 2020. The raw data from the study is deposited here in FMS.zip. The cleaned dataset is available at 10.5281/zenodo.4271730.
   
5. Standalone Solar System for a Community Hall (June 2019 to March 2021) - A standalone solar system was deployed in a Community Hall in Nyabiheke camp, Rwanda in June 2019. The aim of the study was to understand the energy consumption behavior within a set location, and create an evidence base on the value of energy and its benefits for growing cooperatives and learning communities. The standalone system comprised of 2kW of solar panels and 12.2 kWh GEL battery storage capacity. Additional components included a Victron 150/35 charge controller and a Venus GX and 48/3000 MultiPlus Inverter. The system powered four AC 2-pin sockets, a 30 W entrance light, and six 30 W indoor lights. Each light and socket were individually metered and controlled via a remote monitoring unit. This allowed for quotas, maximum draws and periods of use to be remotely controlled.
   
   The study was conducted until March 2021. The raw data from the study is deposited here in Hall.zip. The cleaned dataset until March 2020 is available at 10.5281/zenodo.3949776.
   
6. PV-battery Microgrid (July 2019 to March 2021) - A PV-battery Microgrid was deployed in Kigeme camp, Rwanda in July 2019. The microgrid powered a playground and two nursery buildings. The aim of the study was to identify best practice in the construction, control and operation of a micro-grid as a shared resource, understand optimal design features for user interfaces that allow negotiation over energy priorities and needs and understand community priorities for energy in the context of early years education and the rate of growth in energy utilization. The micro-grid system comprised of a 2.5 kW of solar panels and 21.1 kWh GEL battery storage capacity. Additional components included a Victron 250/60 charge controller, Venus GX 48/1200 MultiPlus Inverter and BMV-700 series battery monitor. Each Nursery building had three classrooms (A, B and C) with separate entrances. Each classroom was fitted with an AC socket, five 10 Watt indoor lights and a 10 Watt outdoor entrance light. A spare socket was located in the first classroom of each nursery building (Classroom A). Two outdoor double sockets were installed at the playground, and fifteen 10 Watt lights were located in the roof structure. Three transmission line poles were fitted with three 10 Watt lights for safety and security purposes, which also enabled them to act as streetlights. Each light and socket was individually monitored and controlled via a programmable remote monitoring unit (RMU). Wireless AC smart meters were used to control and measure power consumption at the socket loads. These meters communicated with the RMU to receive commands and notified the RMU when a command had been received and to transmit usage data. Each light was connected to a CPE (customer-premises equipment) unit, with three lights per CPE, which communicated wirelessly with the RMU. The CPEs received information from the RMU on when to turn the lights on/off and set the brightness. The CPE also monitored the power consumption of the three lights.
   
   The study was conducted until March 2021. The raw data from the study is deposited here in Microgrid.zip. The cleaned dataset until March 2020 is available at 10.5281/zenodo.3949776.
   
7. Standalone solar streetlights (Nepal - June 2019 to October 2020; Rwanda - July 2019 to March 2021) - Seven