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.

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.

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

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

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

**# 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

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...

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.

This map shows where you can get tested for Coronavirus. Most testing points are APPOINTMENT ONLY, while others are available only for medical staff, so check with your doctor (on the phone), the local news, and on local websites for the most up to date information. Markers on this map are accurate to between 5 and 200 meters. You can add drive-thru testing points by creating your own map and sending us the link. We will review it and add it to this map if everything is ok.

Abstract The Coronavirus pandemic aroused the academic community’s concern about indoor air quality. The main way of spreading the disease is through aerosols, with viruses present in particles that remain suspended in the air for long periods. This study seeks to understand the role of natural ventilation in the probability of contagion of the disease in classrooms. Ventilation rates were calculated by the algebraic method for classrooms in two situations: cross ventilation and unilateral ventilation. A reduction in the maximum occupancy of classrooms was proposed, considering a minimum distance of 2 meters between occupants, and maintaining a minimum ventilation rate of 27 m³/h per person. The probability of contagion was calculated for the original and reduced capacities of each room, following the methodology proposed in the literature. Each room was also classified according to its number of air changes per hour. Single-sided ventilation was insufficient to maintain adequate ventilation rates in all cases. For 11 out of the 31 rooms evaluated, a distance of 2 meters between occupants is insufficient to maintain adequate ventilation rates.

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.

Frequency of the statement related with attitude level on COVID-19 (ALC-19).

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.

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 data is the result of a study carried out in March and April of 2020 in response to the novel coronavirus pandemic. The purpose of the study was to evaluate the suitability of common fabrics for homemade face masks. This was conducted in light of the severe PPE (Personal Protective Equipment) shortage caused by the pandemic.

This data was collected using a setup described by Irwin M. Hutton in his 2016 book Handbook of Nonwoven Filter Media, 2nd edition. This testing method and filtration calculation study is consistent with those used in similar studies on particle filtration. For this study, a 1" diameter tubing apparatus was adapted to give access to two P-Trak Ultrafine Particle Counters. Air was pulled through the apparatus at a rate of approximately 16.5 meters per second. Upstream concentrations represent number of ultrafine particles present in the air before it passes through the filter medium. Downstream concentrations represent number of ultrafine particles present after the air has passed through the filter medium. Readings were taken simultaneous 1.5” before and after the filter medium holder. Each data point represents a 10-second average of ultrafine particle concentrations present in passing air. Filtration efficiency was calculated according to Irwin M. Hutten’s formula. For further information on the setup and filtration efficiency (FE) calculations, refer to Chapter 3 of Handbook of Nonwoven Filter Media, 2nd edition, written by Irwin M. Hutton and published by Buttterworth and Heinemann in 2016: https://doi.org/10.1016/B978-0-08-098301-1.00003-4

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.

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

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...

Effect of change in FIES score from wave 1 to wave 2 on PSS, KLC-19 and ALC-19 scores.