Weather and Housing in North America

Exploring the Relationship between Weather and Housing Conditions in 2012

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About this dataset

This comprehensive dataset explores the relationship between housing and weather conditions across North America in 2012. Through a range of climate variables such as temperature, wind speed, humidity, pressure and visibility it provides unique insights into the weather-influenced environment of numerous regions. The interrelated nature of housing parameters such as longitude, latitude, median income, median house value and ocean proximity further enhances our understanding of how distinct climates play an integral part in area real estate valuations. Analyzing these two data sets offers a wealth of knowledge when it comes to understanding what factors can dictate the value and comfort level offered by residential areas throughout North America

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How to use the dataset

This dataset offers plenty of insights into the effects of weather and housing on North American regions. To explore these relationships, you can perform data analysis on the variables provided.

First, start by examining descriptive statistics (i.e., mean, median, mode). This can help show you the general trend and distribution of each variable in this dataset. For example, what is the most common temperature in a given region? What is the average wind speed? How does this vary across different regions? By looking at descriptive statistics, you can get an initial idea of how various weather conditions and housing attributes interact with one another.

Next, explore correlations between variables. Are certain weather variables correlated with specific housing attributes? Is there a link between wind speeds and median house value? Or between humidity and ocean proximity? Analyzing correlations allows for deeper insights into how different aspects may influence one another for a given region or area. These correlations may also inform broader patterns that are present across multiple North American regions or countries.

Finally, use visualizations to further investigate this relationship between climate and housing attributes in North America in 2012. Graphs allow you visualize trends like seasonal variations or long-term changes over time more easily so they are useful when interpreting large amounts of data quickly while providing larger context beyond what numbers alone can tell us about relationships between different aspects within this dataset

Research Ideas

• Analyzing the effect of climate change on housing markets across North America. By looking at temperature and weather trends in combination with housing values, researchers can better understand how climate change may be impacting certain regions differently than others.
• Investigating the relationship between median income, house values and ocean proximity in coastal areas. Understanding how ocean proximity plays into housing prices may help inform real estate investment decisions and urban planning initiatives related to coastal development.
• Utilizing differences in weather patterns across different climates to determine optimal seasonal rental prices for property owners. By analyzing changes in temperature, wind speed, humidity, pressure and visibility from season to season an investor could gain valuable insights into seasonal market trends to maximize their profits from rentals or Airbnb listings over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

License: CC0 1.0 Universal (CC0 1.0) - Public Domain Dedication No Copyright - You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission. See Other Information.

Columns

File: Weather.csv | Column name | Description | |:---------------------|:-----------------------------------------------| | Date/Time | Date and time of the observation. (Date/Time) | | Temp_C | Temperature in Celsius. (Numeric) | | Dew Point Temp_C | Dew point temperature in Celsius. (Numeric) | | Rel Hum_% | Relative humidity in percent. (Numeric) | | Wind Speed_km/h | Wind speed in kilometers per hour. (Numeric) | | Visibility_km | Visibilit...

This represents the Service data reported to the National Transit Database by transit agencies in the 2022, 2023, and 2024 report years. In versions of the data tables from before 2014, you can find data on service in the file called "Transit Operating Statistics: Service Supplied and Consumed." If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

MIB1LM_002 is the Multi-angle Imaging SpectroRadiometer (MISR) Level 1B1 Local Mode Radiance Data version 2. It contains the data numbers (DNs) radiometrically scaled to radiances with no geometric resampling. Multi-angle Imaging SpectroRadiometer (MISR) Level 1B1 Radiance data product contains spectral radiances for all MISR channels. Each value represents the incident radiance averaged over the sensor's total band response. Processing includes both radiance scaling and conditioning steps. Radiance scaling converts the Level 1A data from digital counts to radiances, using coefficients derived from the onboard calibrator (OBC) and vicarious calibrations. The OBC contains Spectralon calibration panels, deployed monthly and reflect sunlight into cameras. The OBC detector standards then measure this reflected light to provide the calibration. No out-of-band correction is done for this product, nor are the data geometrically corrected or resampled. Data collection for this product is ongoing.The MISR instrument consists of nine push-broom cameras that measure radiance in four spectral bands. Global coverage is achieved in nine days. The cameras are arranged with one camera pointing toward the nadir, four forward, and four aftward. It takes seven minutes for all nine cameras to view the same surface location. The view angles relative to the surface reference ellipsoid are 0, 26.1, 45.6, 60.0, and 70.5 degrees. The spectral band shapes are nominally Gaussian, centered at 443, 555, 670, and 865 nm.MISR is designed to view Earth with cameras pointed in 9 different directions. As the instrument flies overhead, each piece of Earth's surface below is successively imaged by all nine cameras in 4 wavelengths (blue, green, red, and near-infrared). The goal of MISR is to improve our understanding of the effects of sunlight on Earth and distinguish different types of clouds, particles, and surfaces. Specifically, MISR monitors the monthly, seasonal, and long-term trends in three areas: 1) amount and type of atmospheric particles (aerosols), including those formed by natural sources and by human activities; 2) amounts, types, and heights of clouds, and 3) distribution of land surface cover, including vegetation canopy structure.

This dataset contains Submersible Ultraviolet Nitrate Analyzer (SUNA) nitrate measurements taken during the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) pilot campaign conducted approximately 300 km offshore of San Francisco over two weeks in October 2021. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. SUNA is a standalone optical nitrate sensor that mounts onto the shipboard CTD rosette cast from the R/V Oceanus. The SUNA measurements are calibrated against bottle nutrient samples taken from the underway flow-through system on the ship and later analyzed with a Lachat Nutrient Analyzer. From the Lachat data, the average concentration of nitrate+nitrite are used for each sample. Data are available in netCDF format.

This dataset contains in-situ measurements of temperature, salinity, and velocity from the Sub-Mesoscale Ocean Dynamics Experiment (S-MODE) conducted approximately 300 km offshore of San Francisco, during an intensive observation period in the fall of 2022. The data are available in netCDF format with a dimension of time. S-MODE aims to understand how ocean dynamics acting on short spatial scales influence the vertical exchange of physical and biological variables in the ocean. The target in-situ quantities were measured by Lagrangian floats, which were deployed from research vessels and retrieved 3-5 days later. The floats follow the 3D motion of water parcels at depths within or just below the mixed layer and carried a CTD instrument to measure temperature, salinity, and pressure, in addition to an ADCP instrument to measure velocity.

Launched in 2009, the Kepler Mission is surveying a region of our galaxy to determine what fraction of stars in our galaxy have planets and measure the size distribution of those exoplanets. Although Kepler completed its primary mission to determine the fraction of stars that have planets in 2013, it is continuing the search, using a more limited survey mode, under the new name K2. The K2 Data Search Service provides the main catalog for all K2 data.

Activity Title: "Data Newsroom: What's the Average Story?"

(This workbook contains eight datasets to practice the activity Describing Data with Averages (Mean, Median, Mode).

Description: Provide students with real-world news data (e.g., COVID cases, temperatures, salaries). Each student team becomes a data journalism unit that must: • Calculate the central tendencies • Interpret how mean, median, and mode tell different stories about the same data • Write a short “news article” summarizing findings.

Outcome: Newspaper-style poster or blog entry comparing the interpretation of each average.

Search Coil Magnetometer (SCM) AC Magnetic Field (16384 samples/s), Level 2, High Speed Burst Mode Data. The tri-axial Search-Coil Magnetometer with its associated preamplifier measures three-dimensional magnetic field fluctuations. The analog magnetic waveforms measured by the SCM are digitized and processed inside the Digital Signal Processor (DSP), collected and stored by the Central Instrument Data Processor (CIDP) via the Fields Central Electronics Box (CEB). Prior to launch, all SCM Flight models were calibrated by LPP team members at the National Magnetic Observatory, Chambon-la-Foret (Orleans). Once per orbit, each SCM transfer function is checked thanks to the onboard calibration signal provided by the DSP. The SCM is operated for the entire MMS orbit in survey mode. Within scientific Regions Of Interest (ROI), burst mode data are also acquired as well as high speed burst mode data. This SCM data set corresponds to the AC magnetic field waveforms in nanoTesla and in the GSE frame. The SCM instrument paper for SCM can be found at http://link.springer.com/article/10.1007/s11214-014-0096-9 and the SCM data product guide at https://lasp.colorado.edu/mms/sdc/public/datasets/fields/.

This dataset details track and roadway mileage/characteristics for each agency, mode, and type of service, as reported to the National Transit Database in the 2022, 2023, and 2024 report years. These data include the types of track/roadway elements employed in transit operation, as well as the length and/or count of certain elements.

NTD Data Tables organize and summarize data from the 2022 - 2024 National Transit Database in a manner that is more useful for quick reference and summary analysis. This dataset is based on the 2022 - 2024 Transit Way Mileage database files.

In years 2015-2021, you can find this data in the "Track and Roadway" data table on NTD Program website, at https://transit.dot.gov/ntd/ntd-data.

In versions of the data tables from before 2015, you can find corresponding data in the file called "Transit Way Mileage - Rail Modes" and "Transit Way Mileage - Non-Rail Modes."

This dataset's 2024 data comes from the NTD as of September 4, 2025.

If you have any other questions about this table, please contact the NTD Help Desk at NTDHelp@dot.gov.

The Highway-Runoff Database (HRDB) was developed by the U.S. Geological Survey, in cooperation with the Federal Highway Administration (FHWA) to provide planning-level information for decision makers, planners, and highway engineers to assess and mitigate possible adverse effects of highway runoff on the Nation’s receiving waters. The HRDB was assembled by using a Microsoft Access database application to facilitate use of the data and to calculate runoff-quality statistics with methods that properly handle censored-concentration data. This data release provides highway-runoff data, including information about monitoring sites, precipitation, runoff, and event-mean concentrations of water-quality constituents. The dataset was compiled from 37 studies as documented in 113 scientific or technical reports. The dataset includes data from 242 highway sites across the country. It includes data from 6,837 storm events with dates ranging from April 1975 to November 2017. Therefore, these data span more than 40 years; vehicle emissions and background sources of highway-runoff constituents have changed markedly during this time. For example, some of the early data is affected by use of leaded gasoline, phosphorus-based detergents, and industrial atmospheric deposition. The dataset includes 106,441 concentration values with data for 414 different water-quality constituents. This dataset was assembled from various sources and the original data was collected and analyzed by using various protocols. Where possible the USGS worked with State departments of transportation and the original researchers to obtain, document, and verify the data that was included in the HRDB. This new version (1.1.0) of the database contains software updates to provide data-quality information within the Graphical User Interface (GUI), calculate statistics for multiple sites in batch mode, and output additional statistics. However, inclusion in this dataset does not constitute endorsement by the USGS or the FHWA. People who use this data are responsible for ensuring that the data are complete and correct and that it is suitable for their intended purposes.

Detailed online statistics for player Brick Mode from world Inabra. View daily activity and session history.

Context:

osu! is a music rhythm game that has 4 modes (check for more info). In this dataset, you can examine the rankings of the standard mode, taken on 30/01/2023 around 3 PM. The ranking is based on pp (performance points) awarded after every play, which are influenced by play accuracy and score; pps are then summed with weights: your top play will award you the whole pp points of the map, then the percentage is decreased (this can maintain balance between strong players and players who play too much). You can find here many other statistics.

Contents:

The dataset contains some columns (see below) reporting statistics for every player in the top 100 of the game in the standard mode. The ranking is ordered by pp. Some players seem to have the same points, but there are decimals that are not shown in the ranking chart on the site

Variables:

• rank: global rank (you can use this like an id too)
• player_name: in-game nickname
• country: country of origin
• accuracy: mean accuracy of your top plays
• play_count: lifetime plays
• level: level (not very influent on stats)
• hours: total hours played
• performance_points: pp which determine the rankings
• ss: number of ss plays (accuracy=100% and no miss)
• s: number of s plays (accuracy>=93% and no miss)
• a: number of a plays (accuracy>=93% but there are misses)
• watched_by: number of replays of the player watched by others

Acknowledgements:

I created this database to use it for my upcoming project in our Data Science.

I used the 2017 osu! rankings and description by Svidon as a reference in order to produce the 2023 osu! ranking in the top 100 as of January 30, 2023

This data will be public and can be accessible on this link https://osu.ppy.sh/rankings/osu/performance.

Here is his kaggle: https://www.kaggle.com/svidon

Here you can find symbols and pictograms for all transport modes to use in your apps, products and other projects. Symbols and icons are available in various formats, while all can be found as vector files that can be opened directly in software such as Adobe Illustrator.

Project Overview

Objective: Analyze Diwali sales data to uncover trends, customer behavior, and sales performance during the festive season. - Tools Used: Python, Pandas, NumPy, Matplotlib, Seaborn

Data Collection and Preparation

Dataset: A dataset containing sales data for Diwali, including details like product categories, customer demographics, sales amounts, discounts, etc.

• **Data Cleaning: **Handle missing values, remove duplicates, and correct any inconsistencies in the data.

- Feature Engineering: Create new features if necessary, such as total sales per customer, average discount per sale, etc.

Exploratory Data Analysis (EDA)

Descriptive Statistics: Calculate basic statistics (mean, median, mode) to get a sense of the data distribution. Visualizations: Sales Trends: Plot sales over time to see how they varied during the Diwali season. Top-Selling Products: Identify the products or categories with the highest sales. Customer Demographics: Analyze sales by age, gender, and location to understand customer behavior. Discount Impact: Evaluate how different discount levels affected sales volume.

Key Findings

Customer Behavior: Insights on which customer segments contributed the most to sales. Sales Performance: Which products or categories had the highest sales, and during which days of Diwali sales peaked. Discount Effectiveness: The impact of discounts on sales and whether higher discounts led to significantly higher sales or not.

Conclusion

Summarize the key insights derived from the EDA. Discuss any patterns or trends that were unexpected or particularly interesting. Provide recommendations for future sales strategies based on the findings. .

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_c34c001a4f526bf33fe6489378051b0f/view

In this study, we generated a matched molecular pair dataset of halogen/deshalogen compounds with reliable binding affinity data and structural binding mode information from public databases. The workflow includes automated system preparation and setup of free energy perturbation relative binding free energy calculations. We demonstrate the suitability of these datasets to investigate the performance of molecular mechanics force fields and molecular simulation algorithms for the purpose of in silico affinity predictions in lead optimization. Our datasets of a total of 115 matched molecular pairs show highly accurate binding free energy predictions with an average error of

This data set accompanies the publication "Quantum-chemical calculation of two-dimensional infrared spectra using localized-mode VSCF/VCI"

It contains:

• xyz files of all considered molecular structures.

• Results data from the harmonic and anharmonic vibrational calculations.

• Data and code for calculating 2D-IR spectra.

The "Dataset_HIR" folder contains the data to reproduce the results of the data mining approach proposed in the manuscript titled "Identification of hindered internal rotational mode for complex chemical species: A data mining approach with multivariate logistic regression model".

More specifically, the folder contains the raw electronic structure calculation input data provided by the domain experts as well as the training and testing dataset with the extracted features.

The "Dataset_HIR" folder contains the following subfolders namely:

1. Electronic structure calculation input data: contains the electronic structure calculation input generated by the Gaussian program

   1.1. Testing data: contains the raw data of all training species (each is stored in a separate folder) used for extracting dataset for training and validation phase.

   1.2. Testing data: contains the raw data of all testing species (each is stored in a separate folder) used for extracting data for the testing phase.

2. Dataset 2.1. Training dataset: used to produce the results in Tables 3 and 4 in the manuscript

   + datasetTrain_raw.csv: contains the features for all vibrational modes associated with corresponding labeled species to let the chemists select the Hindered Internal Rotor from the list easily for the training and validation steps.  
   
   + datasetTrain.csv: refines the datasetTrain_raw.csv where the names of the species are all removed to transform the dataset into an appropriate form for the modeling and validation steps.
   

   2.2. Testing dataset: used to produce the results of the data mining approach in Table 5 in the manuscript.

   + datasetTest_raw.csv: contains the features for all vibrational modes of each labeled species to let the chemists select the Hindered Internal Rotor from the list for the testing step.
   
   + datasetTest.csv: refines the datasetTest_raw.csv where the names of the species are all removed to transform the dataset into an appropriate form for the testing step.
   

Note for the Result feature in the dataset: 1 is for the mode needed to be treated as Hindered Internal Rotor, and 0 otherwise.

This commuter mode share data shows the estimated percentages of commuters in Champaign County who traveled to work using each of the following modes: drove alone in an automobile; carpooled; took public transportation; walked; biked; went by motorcycle, taxi, or other means; and worked at home. Commuter mode share data can illustrate the use of and demand for transit services and active transportation facilities, as well as for automobile-focused transportation projects.

Driving alone in an automobile is by far the most prevalent means of getting to work in Champaign County, accounting for about 64 percent of all work trips in 2024. This is a statistically significant decrease since 2023, which was the first year that matched pre-COVID-19 pandemic levels of driving alone.

The percentage of workers who commuted by all other means to a workplace outside the home also decreased from 2019 to 2021, most of these modes reaching a record low since this data first started being tracked in 2005. All of these modes except public transportation saw increases from 2023 to 2024, but they were not statistically significant. The percentage of people walking to work saw a statistically significant increase from 2022 to 2024.

Meanwhile, the percentage of people in Champaign County who worked at home more than quadrupled from 2019 to 2021, reaching a record high over 18 percent. It is a safe assumption that this can be attributed to the increase of employers allowing employees to work at home when the COVID-19 pandemic began in 2020.

The work from home figure decreased to 11.2 percent in 2023, but which is the first statistically significant decrease since the pandemic began. However, this figure saw a statistically significant increase from 2023 to 2024, rising back from 15.1 percent in 2024. This figure is about 3.3 times higher than 2019, despite the COVID-19 emergency ending in 2023.

Commuter mode share data was sourced from the U.S. Census Bureau’s American Community Survey (ACS) 1-Year Estimates, which are released annually.

As with any datasets that are estimates rather than exact counts, it is important to take into account the margins of error (listed in the column beside each figure) when drawing conclusions from the data.

Due to the impact of the COVID-19 pandemic, instead of providing the standard 1-year data products, the Census Bureau released experimental estimates from the 1-year data in 2020. This includes a limited number of data tables for the nation, states, and the District of Columbia. The Census Bureau states that the 2020 ACS 1-year experimental tables use an experimental estimation methodology and should not be compared with other ACS data. For these reasons, and because data is not available for Champaign County, no data for 2020 is included in this Indicator.

For interested data users, the 2020 ACS 1-Year Experimental data release includes a dataset on Means of Transportation to Work.

Sources: U.S. Census Bureau; American Community Survey, 2024 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (19 November 2024).; U.S. Census Bureau; American Community Survey, 2023 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (18 September 2024).; U.S. Census Bureau; American Community Survey, 2022 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (10 October 2023).; U.S. Census Bureau; American Community Survey, 2021 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (14 October 2022).; U.S. Census Bureau; American Community Survey, 2019 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2018 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using data.census.gov; (26 March 2021).; U.S. Census Bureau; American Community Survey, 2017 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (13 September 2018).; U.S. Census Bureau; American Community Survey, 2016 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (14 September 2017).; U.S. Census Bureau; American Community Survey, 2015 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (19 September 2016).; U.S. Census Bureau; American Community Survey, 2014 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2013 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2012 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2011 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2010 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2009 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2008 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2007 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2006 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).; U.S. Census Bureau; American Community Survey, 2005 American Community Survey 1-Year Estimates, Table S0801; generated by CCRPC staff; using American FactFinder; (16 March 2016).