Number of social distancing violations regressed on linear time, quadratic time, and periodicity.

This dataset is a per-state amalgamation of demographic, public health and other relevant predictors for COVID-19.

Deaths, Infections and Tests by State

The COVID Tracking Project: https://covidtracking.com/data/api

Used positive, death and totalTestResults from the API for, respectively, Infected, Deaths and Tested in this dataset. Please read the documentation of the API for more context on those columns

Predictor Data and Sources

Population (2020)

Density is people per meter squared https://worldpopulationreview.com/states/

ICU Beds and Age 60+

https://khn.org/news/as-coronavirus-spreads-widely-millions-of-older-americans-live-in-counties-with-no-icu-beds/

GDP

https://worldpopulationreview.com/states/gdp-by-state/

Income per capita (2018)

https://worldpopulationreview.com/states/per-capita-income-by-state/

Gini

https://en.wikipedia.org/wiki/List_of_U.S._states_by_Gini_coefficient

Unemployment (2020)

Rates from Feb 2020 and are percentage of labor force
https://www.bls.gov/web/laus/laumstrk.htm

Sex (2017)

Ratio is Male / Female
https://www.kff.org/other/state-indicator/distribution-by-gender/

Smoking Percentage (2020)

https://worldpopulationreview.com/states/smoking-rates-by-state/

Influenza and Pneumonia Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/flu_pneumonia_mortality/flu_pneumonia.htm

Chronic Lower Respiratory Disease Death Rate (2018)

Death rate per 100,000 people
https://www.cdc.gov/nchs/pressroom/sosmap/lung_disease_mortality/lung_disease.htm

Active Physicians (2019)

https://www.kff.org/other/state-indicator/total-active-physicians/

Hospitals (2018)

https://www.kff.org/other/state-indicator/total-hospitals

Health spending per capita

Includes spending for all health care services and products by state of residence. Hospital spending is included and reflects the total net revenue. Costs such as insurance, administration, research, and construction expenses are not included.
https://www.kff.org/other/state-indicator/avg-annual-growth-per-capita/

Pollution (2019)

Pollution: Average exposure of the general public to particulate matter of 2.5 microns or less (PM2.5) measured in micrograms per cubic meter (3-year estimate)
https://www.americashealthrankings.org/explore/annual/measure/air/state/ALL

Medium and Large Airports

For each state, number of medium and large airports https://en.wikipedia.org/wiki/List_of_the_busiest_airports_in_the_United_States

Temperature (2019)

Note that FL was incorrect in the table, but is corrected in the Hottest States paragraph
https://worldpopulationreview.com/states/average-temperatures-by-state/
District of Columbia temperature computed as the average of Maryland and Virginia

Urbanization (2010)

Urbanization as a percentage of the population https://www.icip.iastate.edu/tables/population/urban-pct-states

Age Groups (2018)

https://www.kff.org/other/state-indicator/distribution-by-age/

School Closure Dates

Schools that haven't closed are marked NaN https://www.edweek.org/ew/section/multimedia/map-coronavirus-and-school-closures.html

Note that some datasets above did not contain data for District of Columbia, this missing data was found via Google searches manually entered.

Number of social distancing violations regressed on the number of people on the street and each of the other variables.

This graph represents the distribution of the dwellings where French people live the lockdown of March 17 due to coronavirus (COVID-19) in March 2020, by surface area in square meters. At that time 34 percent of respondents were confined in dwellings with a surface area varying between 80 and 109 square meters.

For more information on the coronavirus pandemic (COVID-19), please see our page: facts and figures about COVID-19 coronavirus.

Since the beginning of the pandemic, the transmission modes of SARS-CoV-2—particularly the role of aerosol transmission—has been much debated. Accumulating evidence suggests that SARS-CoV-2 can be transmitted by aerosols, and not only via larger respiratory droplets. In this study, we quantified SARS-CoV-2 in air surrounding 14 test subjects in a controlled setting. All subjects had SARS-CoV-2 infection confirmed by a recent positive PCR test and had mild symptoms when included in the study. RT-PCR and cell culture analyses were performed on air samples collected at distances of one, two, and four meters from test subjects. Oronasopharyngeal samples were taken from consenting test subjects and analyzed by RT-PCR. Additionally, total aerosol particles were quantified during air sampling trials. Air viral concentrations at one-meter distance were significantly correlated with both viral loads in the upper airways, mild coughing, and fever. One sample collected at four-meter distance was R...

The following report outlines the workflow used to optimize your Find Hot Spots result:Initial Data Assessment.There were 2933 valid input features.There were 3108 valid input aggregation areas.There were 3108 valid input aggregation areas.There were 66 outlier locations; these will not be used to compute the optimal fixed distance band.Incident AggregationAnalysis was based on the number of points in each polygon cell.Analysis was performed on all aggregation areas.The aggregation process resulted in 3108 weighted areas.Incident Count Properties:Min0.0000Max0.0015Mean0.0001Std. Dev.0.0001Scale of AnalysisThe optimal fixed distance band was based on the average distance to 30 nearest neighbors: 150682.0000 Meters.Hot Spot AnalysisThere are 865 output features statistically significant based on a FDR correction for multiple testing and spatial dependence.OutputRed output features represent hot spots where high incident counts cluster.Blue output features represent cold spots where low incident counts cluster.

This graph illustrates the average surface area of the dwellings in which French people live during the containment of March 17 due to the coronavirus (COVID-19) in March 2020, by region and in square meters. At that time in the region of Bourgogne-Franche-Comté, French people were confined in dwellings with an average surface area of 108 square meters.

For more information on the coronavirus pandemic (COVID-19), please see our page: Facts and figures about COVID-19 coronavirus

With the third-highest number of confirmed COVID-19 cases worldwide, Brazil was the country that required the largest volume of oxygen in Latin America. As of ***************, the Portuguese-speaking nation needed nearly *** million cubic meters of oxygen per day to treat its patients. Meanwhile, Mexico needed close to *** thousand cubic meters of oxygen per day. Most of the countries in the region required less than *** thousand cubic meters of oxygen per day. A critical situation Medical oxygen is pivotal for treating patients affected by the COVID-19 disease. The virus can cause pneumonia, which can lead to acute respiratory distress syndrome (lung failure) and eventually death. Medical oxygen enables patients to receive the oxygen required for normal bodily function. With more than *** million cases worldwide, oxygen demand is at an all-time high. As of ***********, India required the most oxygen at more than * million cylinders per day. It is not just oxygen The shortfall in the amount of medical oxygen in Brazil is coupled with a general lack of resources. In 2019, the South American country had only **** intensive care unit (ICU) beds per 100,000 population. In addition, Brazil registered just over ** ventilators per 100,000 inhabitants that same year. Unfortunately, as one of the most affected countries worldwide, this is not enough to meet the soaring demand.

Snapshot img

The smart meter market share should rise by USD 11.19 billion from 2021 to 2025 at a CAGR of 7.76%.

This smart meter market research report provides valuable insights on the post COVID-19 impact on the market, which will help companies evaluate their business approaches. Furthermore, this report extensively covers market segmentation by end-user (residential, commercial, and industrial) and geography (APAC, Europe, North America, South America, and MEA). The smart meter market report also offers information on several market vendors, including Aichi Tokei Denki Co. Ltd., Badger Meter Inc., Honeywell International Inc., Itron Inc., Kamstrup AS, Landis+Gyr AG, Schneider Electric SE, Siemens AG, Wasion Holdings Ltd., and Xylem Inc. among others.

What will the Smart Meter Market Size be in 2021?

To Unlock the Smart meter Market Size for 2021 and Other Important Statistics, Download the Free Report Sample!

Smart Meter Market: Key Drivers and Trends

The growing investment in smart grid projects is notably driving the smart meter market growth, although factors such as high initial capital requirement may impede the market growth. Our research analysts have studied the historical data and deduced the key market drivers and the COVID-19 pandemic impact on the smart meter industry. The holistic analysis of the drivers will help in predicting end goals and refining marketing strategies to gain a competitive edge.

This smart meter market analysis report also provides detailed information on other upcoming trends and challenges that will have a far-reaching effect on market growth. The actionable insights on the trends and challenges will help companies evaluate and develop growth strategies for 2021-2025.

Who are the Major Smart Meter Market Vendors?

The report analyzes the market’s competitive landscape and offers information on several market vendors, including:

Aichi Tokei Denki Co. Ltd.
Badger Meter Inc.
Honeywell International Inc.
Itron Inc.
Kamstrup AS
Landis+Gyr AG
Schneider Electric SE
Siemens AG
Wasion Holdings Ltd.
Xylem Inc.

The vendor landscape of the smart meter market entails successful business strategies deployed by the vendors. The smart meter market is fragmented and the vendors are deploying various organic and inorganic growth strategies to compete in the market.

To make the most of the opportunities and recover from post COVID-19 impact, market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments.

Download a free sample of the smart meter market forecast report for insights on complete key vendor profiles. The profiles include information on the production, sustainability, and prospects of the leading companies.

Which are the Key Regions for Smart Meter Market?

For more insights on the market share of various regions Request for a FREE sample now!

36% of the market’s growth will originate from APAC during the forecast period. China and Japan are the key markets for smart meters in APAC.

The report offers an up-to-date analysis of the geographical composition of the market. APAC has been recording a significant growth rate and is expected to offer several growth opportunities to market vendors during the forecast period. Stringent government policies towards curbing the wastage of water and electricity will facilitate the smart meter market growth in APAC over the forecast period. The report offers an up-to-date analysis of the geographical composition of the market, competitive intelligence, and regional opportunities in store for vendors.

What are the Revenue-generating End-user Segments in the Smart Meter Market?

To gain further insights on the market contribution of various segments Request for a FREE sample

The smart meter market share growth by the residential segment has been significant. This report provides insights on the impact of the unprecedented outbreak of COVID-19 on market segments. Through these insights, you can safely deduce transformation patterns in consumer behavior, which is crucial to gauge segment-wise revenue growth during 2021-2025 and embrace technologies to improve business efficiency.

This report provides an accurate prediction of the contribution of all the segments to the growth of the smart meter market size. Furthermore, our analysts have indicated actionable market insights on post COVID-19 impact on each segment, which is crucial to predict change in consumer demand.

    Smart Meter Market Scope




    Report Coverage


    Details




    Page number


    120




    Base year


    2020




    Forecast period


    2021-2025




    Growth momentum & CAGR


    Accelerate at a CAGR of 7.76%




    Market growth 2021-2025


    USD 11.19 billion




    Market structure

According to Cognitive Market Research, the global Smart meter data management market size was USD 1565.2 million in 2024. It will expand at a compound annual growth rate (CAGR) of 18.20% from 2024 to 2031.

North America held the major market share for more than 40% of the global revenue with a market size of USD 626.08 million in 2024 and will grow at a compound annual growth rate (CAGR) of 16.4% from 2024 to 2031.
Europe accounted for a market share of over 30% of the global revenue with a market size of USD 469.56 million.
Asia Pacific held a market share of around 23% of the global revenue with a market size of USD 360.00 million in 2024 and will grow at a compound annual growth rate (CAGR) of 20.2% from 2024 to 2031.
Latin America had a market share of more than 5% of the global revenue with a market size of USD 78.26 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.6% from 2024 to 2031.
Middle East and Africa had a market share of around 2% of the global revenue and was estimated at a market size of USD 31.30 million in 2024 and will grow at a compound annual growth rate (CAGR) of 17.9% from 2024 to 2031.
The software held the highest Smart meter data management market revenue share in 2024.

Market Dynamics of Smart meter data management Market

Key Drivers for Smart meter data management Market

Utility industry transformations to increase the demand globally

The utility industry is undergoing significant transformations driven by the need for increased efficiency, sustainability, and customer engagement. Innovations in smart grid technologies, data analytics, and renewable energy integration are reshaping how utilities operate. The adoption of smart meters and advanced data management systems enhances real-time monitoring and decision-making, enabling more efficient resource distribution and improved customer service. Regulatory pressures and global sustainability goals further accelerate this shift, pushing utilities towards greener practices and smarter infrastructure. These changes are expanding market opportunities globally, as utilities and consumers alike seek to optimize energy use and reduce environmental impact.

Increased demand for energy efficiency to propel market growth

The growing demand for energy efficiency is significantly propelling market growth for smart meter data management systems. As energy costs rise and environmental concerns intensify, both consumers and utilities are increasingly prioritizing energy-saving measures. Smart meters provide real-time data on energy consumption, enabling more precise management and optimization. This data helps identify inefficiencies, reduce waste, and support targeted conservation efforts. Consequently, the focus on improving energy efficiency drives the adoption of advanced smart metering solutions, which offer enhanced monitoring, analysis, and control capabilities. This heightened awareness and need for efficiency fuel market expansion and innovation in energy management technologies.

Restraint Factor for the Smart meter data management Market

Operational disruptions to limit the sales

Operational disruptions can significantly limit sales in the smart meter data management market. Implementing new technologies often requires extensive system integration and adaptation, which can interrupt existing processes and workflows. These disruptions may lead to temporary inefficiencies, increased costs, and resistance from staff and stakeholders. Additionally, the transition phase might involve steep learning curves and potential technical issues, further complicating deployment. Such challenges can delay or deter organizations from adopting smart meter solutions, impacting overall sales. To mitigate these effects, companies must focus on seamless integration, comprehensive training, and robust support systems to minimize operational disruptions and maintain market momentum.

Impact of Covid-19 on the Smart meter data management Market

The COVID-19 pandemic negatively impacted the smart meter data management market, causing significant disruptions. Lockdowns and social distancing measures slowed down the installation and maintenance of smart meters, leading to delays in project timelines and reduced market activity. Economic uncertainties and budget constraints faced by utilities and businesses resulted in postponed or canceled investments in new technologies. Additionally, t...

Most COVID-19 infections are caused by airborne corona viruses. A model based on empirical and theoretical studies of dispersion of particles in the air was used to estimate a person’s virus exposures during different times and distances from a virus infected person. Participants judged perceived virus exposure at different interpersonal distances in a face to face conversation with an infected person. The smallest average face to face distance from an infected person without a mask that a participant judged as sufficiently safe was 13.5 meters. A majority (75%) of the participants underestimated the effect on virus exposure following a change of interpersonal distance. By way of contrast, judgments of exposure as a function of the duration of a conversation were unbiased. The results are important for administrators and communications to the general public about social distancing and infection risks.

Hospitality operators around the world have increased their focus on health and hygiene as a result of the coronavirus (COVID-19) pandemic. As of June 2020, a global survey was conducted to determine the share of hospitality operators who spaced their tables and chairs in dining venues at least *** meters apart and frequently disinfected their public areas. The vast majority of respondents, ** percent, reported having done so, while only ***** percent of respondents reported having done otherwise.

The dataset contains temperature measurements on the surface of the face taken on 109 people. Each patient (identified by Patient ID in the dataset) acclimatized in a room with a temperature of 22-24 degrees Celsius. Then the person completed a survey, during which they provided their: • age (column Survey - age [years]), • gender (column Survey - Gender), • temperature measurement using a pyrometer thermometer (column Survey - temperature [°C]), • and pulse measurement using a pulse oximeter (column Survey - measured pulse [BPM]). After that, the examined person stood in front of the contactless temperature measurement system (using a thermal camera), which was continuously calibrated to the black body at a distance of 1.5-3 meters (column Distance between camera and patient [m]). Then, several hundred temperature measurements were taken on each person in the following ways: • Median temperature on face [°C] • Median temperature on face, 1% of pixels with max temperature [°C] • Median temperature on face, 5% of pixels with max temperature [°C] • Median temperature on face, 10% of pixels with max temperature [°C] • Median temperature in the center of the eyes (3x3 pixels) [°C] • Median temperature measured at the corners of the eyes (3x3 pixels) [°C] Additionally, the system automatically estimated: • the age of the examined person (column Estimated Age [years]), • the pulse of the examined person (column Estimated Pulse [BPM]), • and gender (Estimated Gender). According to [1], the measured temperature on the surface of the face is influenced by the age of the measured person. As part of the project, a Binary Regression Tree was developed, which considers (estimated) age when calculating the temperature on the surface of the face (column Temperature calculated by Binary Tree Regression algorithm [°C]). [1] Cheung, Ming & Chan, Lung & Lauder, I & Kumana, Cyrus. (2012). Detection of body temperature with infrared thermography: accuracy in detection of fever. Hong Kong medical journal = Xianggang yi xue za zhi / Hong Kong Academy of Medicine. 18 Suppl 3. 31-4.

This layer shows population broken down by race and Hispanic origin. This is shown by tract, county, and state centroids. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. This layer is symbolized to show the predominant race living within an area, and the total population in that area. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Current Vintage: 2019-2023ACS Table(s): B03002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer is updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases, specifically, the National Sub-State Geography Database (named tlgdb_(year)_a_us_substategeo.gdb). Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines erased for cartographic and mapping purposes. For census tracts, the water cutouts are derived from a subset of the 2020 Areal Hydrography boundaries offered by TIGER. Water bodies and rivers which are 50 million square meters or larger (mid to large sized water bodies) are erased from the tract level boundaries, as well as additional important features. For state and county boundaries, the water and coastlines are derived from the coastlines of the 2023 500k TIGER Cartographic Boundary Shapefiles. These are erased to more accurately portray the coastlines and Great Lakes. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters).The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

Analysis of Spanish Apartment Pricing and Size Post-COVID-19

Investigating the Impact of the Pandemic

By [source]

About this dataset

This dataset provides an in-depth insight into Spanish apartment prices, locations and sizes, offering a comprehensive view of the effects of the Covid-19 crisis in this market. By exploring the data you can gain valuable knowledge on how different variables such as number of rooms, bathrooms, square meters and photos influence pricing, as well as key details such as description and whether or not they are recommended by reviews. Furthermore, by comparing average prices per square meter regionally between different areas you can get a better understanding of individual apartment value changes over time. Whether you are looking for your dream home or simply seeking to understand current trends within this sector this dataset is here to provide all the information necessary for both people either starting or already familiar with this industry

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How to use the dataset

This dataset includes a comprehensive collection of Spanish apartments that are currently up for sale. It provides valuable insight into the effects of the Covid-19 pandemic on pricing and size. With this guide, you can take advantage of all the data to explore how different factors like housing surface area, number of rooms and bathrooms, location, number of photos associated with an apartment, type and recommendations affect price.

• First off, you should start by taking a look at summary column which summarizes in one or two lines what each apartment is about. You can quickly search some patterns which could give important information about the market current situation during COVID-19 crisis.

• Explore more in depth each individual apartment by looking at its description section for example if it refers to particular services available like swimming pool or gymnasiums . Consequently those extra features usually bumps up the prices higher since buyers are keen to have such luxury items included in their purchase even if it’s not so affordable sometimes..

• Start studying locationwise since it might gives hint as to what kind preof city we have eirther active market in terms equity investment , home stay rental business activities that suggest opportunities for considerable return on investment (ROI). Even further detailed analysis such as comparing net change over time energy efficient ratings electrical or fuel efficiency , transport facilities , educational level may be conducted when choosing between several apartments located close one another ..

• Consider multiple column ranging from price value provided (price/m2 )to size sqm surface area measure and count number of rooms & bathrooms . Doing so will help allot better understanding whether purchasing an unit is worth expenditure once overall costs per advantages estimated –as previously acknowledged apps features could increase prices significantly- don’t forget security aspect major item critical home choice making process affording protection against Intruders ..

• An interesting but tricky part is Num Photos how many were included –possibly indicates quality build high end projects appreciate additional gallery mentioning quite informative panorama around property itself - while recomendation customarily assumes certain guarantees warranties unique promise provided providing aside prospective buyer safety issues impose trustworthiness matters shared among other future residents …

• Finally type & region column should be taken into account reason enough different categories identifies houses versus flats diversely built outside suburban villas contained inside specially designed mansion areas built upon special requests .. Therefore usage those two complementary field help finding right desired environment accompaniments beach lounge bar attract nature lovers adjacent mountainside

Research Ideas

• Creating an interactive mapping tool that showcases the average prices per square meter of different cities or regions in Spain, enabling potential buyers to identify the most affordable areas for their desired budget and size.
• Developing a comparison algorithm that recommends the best options available depending on various criteria such as cost, rooms/bathrooms, recommended status, etc., helping users make informed decisions when browsing for apartments online.
• Constructing a model that predicts sale prices based on existing data trends and analyses of photos and recommendations associated wit...

Snapshot img

Gas Meter Market - 2021-2025

The gas meter market size is expected to reach a value of USD 3.43 billion, at a CAGR of 8.53%, during 2021-2025. This research study helps in a deep understanding of the underlying forces driving the market growth and current and potential target customers across segmentations. According to our comprehensive survey, factors such as smart meters enable efficient use of gas are projected to significantly support market growth during the forecast period. Our research experts have thoroughly evaluated and covered the latest trends and challenges that will have a far-reaching effect on the market growth.

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Gas Meter Market Segments

From this Technavio report, get actionable insights on the gas meter market segments to generate successful ROIs and focus your business strategy efforts where they are most likely to be effective. Navigate through market segmentation by product (smart gas meter and basic gas meter) and geography (North America, Europe, APAC, MEA, and South America) in this gas meter market report to pursue growth opportunities.

Also, our market research experts have evaluated the impact of COVID-19 across market segments for our clients to understand the long-term business implications and foresee opportunities for subsequent recovery. This research report entails a thorough qualitative and quantitative analysis on the post-pandemic gas meter market predictions on consumer demand changes for 2021-2025.

Gas Meter Market Vendors and Competitive Analysis

    Technavio research specialists have included significant well-thought-out business planning approaches of key players in this report. The gas meter market is fragmented and the vendors are deploying organic and inorganic growth strategies to gain a competitive advantage.

    The unprecedented outbreak of COVID-19 last year impacted market segments that has had a ripple effect on various stakeholders. To make the most of the opportunities and recover from post COVID-19 impact, the market vendors should focus more on the growth prospects in the fast-growing segments, while maintaining their positions in the slow-growing segments. Get the full report to understand the COVID-19 impact on the growth of the gas meter market share during 2021-2025.

    The gas meter market forecast report comprises complete key vendor profiles. The profiles include information on the production, sustainability, prospects of the leading companies, and other crucial vendor landscape analysis.

Gas Meter Market - Region Opportunities 2021-2025

34% of the gas meter market growth will originate from North America during the forecast period. The US is the key market for gas meter market in North America. This report encompasses exclusive information on potential business locations and understand the demographics of current and prospective customers.

To unlock infomation on emerging business opportunities across regions, Request for a FREE Sample!

North America has been exhibiting a significant growth rate for gas meter market vendors. Factors such as the increased production of natural gas are accelerating the gas meter market growth in North America. To view our in-depth analytical review on the micro and macroeconomic factors impacting businesses in the regions click on the image above.

The gas meter market share growth in North America will be slower than the growth of the market in regions such as APAC and MEA. The geographical segmentation in the report has assessed and included details on the competitive intelligence and regional opportunities in store for vendors.

Gas Meter Market Insights by Product

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The gas meter market share growth by the smart gas meter segment will be significant during the forecast period. The smart gas meter segment is expected to grow rapidly during the forecast period as the end-users are increasingly preferring smart gas meters compared to basic gas meters. This report provides an accurate prediction of the contribution of all the segments to the growth of the gas meter market size.

From the gas meter market segmentation insights, players can achieve maximum market response by understanding the target consumers. The analytical data on the segmentations will allow vendors to position their services and products among the right audiences and gain significant exposure and growth. Also, get updated actionable market insights on post COVID-19 impact on each segment.

Gas Meter Market Drivers & Trends

While it is crucial to have a solid understanding of the drivers and trends, it is also imperative that the market challenges are recognized to improvize business planning and s

**# Context **Close contact status of corona infected people in Japan

This dataset is Organization of the Ministry of Health, Labour and Welfare　in Japan. About coronavirus It is a virus that causes infections widely among humans and animals. There are six known causes of infectious disease in humans, but SARS-CoV (severe acute respiratory syndrome coronavirus) and MERS-CoV (middle east respiratory syndrome), which can cause serious respiratory illness Other than coronavirus, infections are limited to non-severe symptoms such as a common cold.

This is not a risk data set. Created to encourage "appropriate" response by conducting fact-based analysis without being overly afraid of an unknown disease

**# Content **The data is available from 7th March, 2020.

**# Column Description ** No - Fixed date - Age - sex - residence - The area I live in Surrounding patients -Impact on surrounding patients Close contact situation- It represents Close contact situation

*What is a definition ”Close Contact" "Close Contact" refers to the following range of those who have contacted since the day the "patient (confirmed)"

・ Living with or prolonged contact with a suspected new coronavirus infection (in a car, on an airplane, etc.) Including)

・Examining, nursing or caring for patients suspected of having new type coronavirus infection without appropriate infection protection.

・Direct contact with contaminants such as respiratory tract secretions or body fluids of those suspected of having the novel coronavirus infection Those who are likely to have

・Other: Necessary at a distance (approximately 2 meters) that can be touched by hand or face-to-face conversation. Persons who contacted the “patient (confirmed example)” without any precautionary measures (such as patient symptoms and mask use) Comprehensively determine the infectivity of patients).

*Quote NIID https://www.niid.go.jp/niid/ja/diseases/ka/corona-virus/2019-ncov/2484-idsc/9357-2019-ncov-02.html

**# Acknowledgements **To everyone, including doctors, nurses and volunteers, who are fighting the coronavirus

**# Inspiration **Some insights could be Changes in number of affected cases over time Change in cases over time at country level Latest number of affected cases

**# Data at individual level obtained from the below **https://www.mhlw.go.jp/stf/seisakunitsuite/bunya/0000121431_00086.html

This is old dataset and not being updated now

According to Cognitive Market Research, the global smart meter system market size is USD 22541.2 million in 2024 and will expand at a compound annual growth rate (CAGR) of 36.20% from 2024 to 2031.

North America held the major market of more than 40% of the global revenue with a market size of USD 9016.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 34.4% from 2024 to 2031.
Europe accounted for a share of over 30% of the global market size of USD 6762.36 million.
Asia Pacific held the market of around 23% of the global revenue with a market size of USD 5184.48 million in 2024 and will grow at a compound annual growth rate (CAGR) of 38.2% from 2024 to 2031.
Latin America market of more than 5% of the global revenue with a market size of USD 1127.06 million in 2024 and will grow at a compound annual growth rate (CAGR) of 35.6% from 2024 to 2031.
Middle East and Africa held the major market of around 2% of the global revenue with a market size of USD 450.82 million in 2024 and will grow at a compound annual growth rate (CAGR) of 35.9% from 2024 to 2031.
The residential held the highest smart meter system market revenue share in 2024.

Market Dynamics of Smart Meter System Market

Key Drivers for Smart Meter System Market

Rapid Growth in Smart Meter Adoption by Government to Increase the Demand Globally

The rapid growth in smart meter adoption by governments worldwide is driving the expansion of the smart meter system market. Smart meters, which provide real-time data on energy usage, enable better energy management and efficiency. Governments are investing in these technologies to meet sustainability goals, reduce energy consumption, and enhance grid reliability. This trend is fueled by policy mandates, environmental concerns, and advancements in IoT and data analytics. As a result, the smart meter market is experiencing significant growth, with increased deployment in residential, commercial, and industrial sectors?.

Growing Demand for Energy Efficiency to Propel Market Growth

The growing demand for energy efficiency is driving the expansion of the smart meter system market. Smart meters enable precise monitoring and management of energy consumption, aiding consumers and utilities in optimizing usage and reducing waste. Enhanced data collection and real-time analytics provided by these systems support better energy distribution and fault detection. Additionally, regulatory mandates and increasing awareness of environmental sustainability are further propelling market growth. As a result, the smart meter market is poised for significant advancements, offering substantial benefits in energy conservation and cost savings.

Restraint Factor for the Smart Meter System Market

High Deployment Cost to Limit the Sales

High deployment costs in the smart meter system market act as a significant restraint. The installation and maintenance of advanced metering infrastructure require substantial investment in hardware, software, and skilled labor. Smaller utilities and developing regions often struggle with the financial burden, hindering widespread adoption. Additionally, integrating smart meters with existing grid infrastructure can be complex and costly. These financial and logistical challenges slow down the deployment rate, limiting market growth and delaying the benefits of smart grid technologies.

Impact of Covid-19 on the Smart Meter System Market

The COVID-19 pandemic significantly impacted the Smart Meter System market, accelerating its growth. Lockdowns and remote work increased energy consumption monitoring needs, boosting demand for smart meters. Utilities adopted smart meters for real-time data to manage fluctuating energy usage effectively. The pandemic highlighted the importance of efficient energy management, driving investments in smart grid technologies. Despite supply chain disruptions, the market saw a surge due to heightened awareness of energy conservation and the need for advanced metering infrastructure for better energy distribution and management. Introduction of the Smart Meter System Market

A smart meter system is an advanced energy meter that provides real-time monitoring, management, and communication of electricity usage to utilities and consumers. The smart meter system market is growing and is driven by increasing smart city initiatives. Smart meters provide real-time energy consumption data, enabling efficient...

The data underpins a study which aimed to investigate the impact of remoteness on the travel time and population catchment for all COVID-19 point-of-care-test sites within Victoria during Stage 4 restrictions during July 2020.

There are two files 'mesh_block_summary' and 'testing_site_summary'.

In relation to 'mesh_block_summary', please consider the points below. - The data provides the average travel time (in minutes) and distance (in metres) to the closest point-of-care-test site for each mesh block. MB_CODE16: Mesh block identifier Duration: Distance in metres Distance: Travel time in minutes MB_Category_Name_2016: Mesh block category Dwelling: Number of dwellings Person: Number of people

In relation to 'testing_site_summary', please consider the points below. - The data provides the average travel time (in minutes) and distance (in metres) for mesh blocks which were closest (based on travel time) to each test site. Site_Name: Name of point-of-care-test site Facility: Type of site Website: Site website COVID_Lat: Latitude coordinate COVID_Long: Longitude coordinate Dwelling: Number of dwellings within mesh blocks which were closest (based on travel time) to each test site. Population: Number of people within mesh blocks which were closest (based on travel time) to each test site. Mean_distance: Average distance (in metres) for closest mesh blocks Mean_duration: Average travel time (in minutes) for closest mesh blocks N_mesh_blocks: Number of mesh blocks which are closest Mean_catchment_IRSD: Mean 'Index of Relative Socioeconomic Disadvantage' for closest mesh blocks

The methodology to derive the data above has been detailed within the reference below: Lakhani A, Wollersheim D. COVID-19 test sites in Victoria approaching Stage 4 restrictions: evaluating the relationship between remoteness, travel time and population serviced. Aust N Z J Public Health. 2021 Dec;45(6):628-636. doi: 10.1111/1753-6405.13154. Epub 2021 Oct 28. PMID: 34709703; PMCID: PMC8652517.

This study aimed to analyze the effect of sociodemographic characteristics on COVID-19 in-hospital mortality in Ecuador from March 1 to December 31, 2020. This retrospective longitudinal study was performed with data from publicly accessible registries of the Ecuadorian National Institute of Statistics and Censuses (INEC). Data underwent a competing risk analysis with estimates of the cumulative incidence function (CIF). The effect of covariates on CIFs was estimated using the Fine-Gray model and results were expressed as adjusted subdistribution hazard ratios (SHR). The analysis included 30,991 confirmed COVID-19 patients with a mean age of 56.57±18.53 years; 60.7% (n = 18,816) were men and 39.3% (n = 12,175) were women. Being of advanced age, especially older than or equal to 75 years (SHR = 17.97; 95%CI: 13.08-24.69), being a man (SHR = 1.29; 95%CI: 1.22-1.36), living in rural areas (SHR = 1.18; 95%CI: 1.10-1.26), and receiving care in a public health center (SHR = 1.64; 95%CI: 1.51-1.78) were factors that increased the incidence of death from COVID-19, while living at an elevation higher than 2,500 meters above sea level (SHR = 0.69; 95%CI: 0.66-0.73) decreased this incidence. Since the incidence of death for individuals living in rural areas and who received medical care from the public sector was higher, income and poverty are important factors in the final outcome of this disease.