Healthcare Utilization, Air Quality, and Population Analysis
Using Data Science and GIS to Forecast Future Trends from Historic Data
Charles A. Hulebak
Introduction and Overview
The intention of developing this digital product on the RStudio platform is to provide a repeatable methodology that can be replicated for similar systems-of-systems analysis from a macro perspective. Applications, analysis, data science, and analytics platforms provide us with the ability to apply repeatable methodologies to achieve a variety of organizational functions while providing the ability to guide investments and future decision-making. The scientific objective of this investigation seeks to determine the relationships between the systems and how one system (outdoor air quality), can influence changes in another system (healthcare utilization).
We have the ability to hypothesize that pollution is not healthy for the environment or human health, however that generalization can be more specifically understood with data science, analysis, mathematical, and statistical processes.
This analysis includes three objectives:
- Use historic and present data to forecast future trends
- Discover scientific relationships between outdoor air quality, healthcare utilization, and population
- Provide data visualization on a geographical information system (GIS)
These objectives are supported using statistical analysis, mathematics, and data science techniques. Present and historic data is used to obtain forecasts that have not been obtained or published, which provides an opportunity for to guide future decisions for the environment and human health.
The datasets used for this analysis include:
- Population data for the State of California by county1
- Outdoor Air quality data2
- Outdoor Air quality sensor locations3
- Health utilization for the State of California4
- Cartographic boundary maps for the United States and State of California by county5,6
The RStudio platform provides the ability to show the data and processes which accompany this analysis, as well as create a platform that can be continually developed for improvements, updating, and modifying data.
The process for analyzing this data is shown in the following diagram.
Process Diagram
Standardizing the Datasets
The processes for standardizing the data for this report included:
- Exporting comma delimited (.csv) data from Excel formatted (.xlsx) multiple sheet sets
- Converting data types, such as characters to numbers, integers, and matrices
- Modifying dataset names to reference one another, such as names listed as “County, California” to the simple form of the “county” name
- Combining (or merging) multiple referenced sheets into a common file
- Combining multiple datasets into a single dataset
- Exporting files as comma delimited (.csv) files for use in external programs to this application
Population data and forecasting
Estimates of county population totals have been collected by the U.S. Census Bureau for the years specified in this project.
Import Population Data and Review Summary Statistics Summary statistics provide the ability to review the data, including the data classification, as well as minimum, maximum, mean, median, and quartiles (1st/3rd).
options(width = 60)
Pop <- read.table("01-Population/CAPop_2012-2021.csv",header=TRUE,sep=",")
summary(Pop[1:12])## county X2010 X2011
## Length:59 Min. : 1161 Min. : 1093
## Class :character 1st Qu.: 50329 1st Qu.: 49995
## Mode :character Median : 181136 Median : 180936
## Mean : 1265068 Mean : 1275877
## 3rd Qu.: 703413 3rd Qu.: 711349
## Max. :37319502 Max. :37638369
## X2012 X2013 X2014
## Min. : 1110 Min. : 1128 Min. : 1080
## 1st Qu.: 49518 1st Qu.: 49326 1st Qu.: 49261
## Median : 180575 Median : 181481 Median : 183108
## Mean : 1286400 Mean : 1296976 Mean : 1308372
## 3rd Qu.: 719408 3rd Qu.: 725354 3rd Qu.: 734392
## Max. :37948800 Max. :38260787 Max. :38596972
## X2015 X2016 X2017
## Min. : 1077 Min. : 1047 Min. : 1111
## 1st Qu.: 49290 1st Qu.: 49546 1st Qu.: 49828
## Median : 184569 Median : 185976 Median : 188679
## Mean : 1319256 Mean : 1327699 Mean : 1334186
## 3rd Qu.: 743663 3rd Qu.: 750358 3rd Qu.: 756098
## Max. :38918045 Max. :39167117 Max. :39358497
## X2018 X2019 X2020
## Min. : 1089 Min. : 1129 Min. : 1198
## 1st Qu.: 50042 1st Qu.: 50192 1st Qu.: 50422
## Median : 190746 Median : 192843 Median : 191114
## Mean : 1337681 Mean : 1339397 Mean : 1338974
## 3rd Qu.: 760586 3rd Qu.: 764360 3rd Qu.: 771485
## Max. :39461588 Max. :39512223 Max. :394997382021 Population Forecast
The population data initially obtained was for the duration of 2012-2020, however this project was seeking to create forecasted data for 2021.
There are a few different statistical methods that can be used to forecast future data, and a common method used for population includes the Modified Exponential Equation to measure the population over time. The average of the years will be taken into consideration to provide the anticipated result for the final year being calculated. The formula and processes are shown below:
\[ModifiedExponential Equation
(population) = target year + (Year 1 + Year 2)^{Time}\]
These process steps can be completed internally using the R platform, as
well as using a multitude of software and database applications.
This data was produced and extracted to a new .csv file for the year 2021 providing the following results.
## X2021
## Min. : 1056
## 1st Qu.: 51462
## Median : 194970
## Mean : 1338910
## 3rd Qu.: 759332
## Max. :39497840Air Quality
Outdoor Air Quality Sensors
The United States Environmental Protection Agency (EPA) has implemented and managed a variety of outdoor air quality sensors. The data collected includes the geolocations and the type of air quality that is being measured. The datasets are available for download, with the link provided within the reference section.
knitr::include_graphics("06-Images/03-EPA.jpg", dpi = 150)
Source: Environmental Protection Agency (EPA) Outdoor Air Quality
Data Download Website2
The outdoor air quality data consists of fourteen categories across the country, however these vary by county. Within the State of California, there was limited data associated with sulfphur dioxide (SO2) and lead particulates, which was insufficient to perform statistical and mathematical analysis to determine strong correlations. There was sufficient data to provide analysis with ten sensors for 202 county entries between the years of 2012-2021.
A description of the outdoor air quality sensors include:
• Ozone (O3) 2nd Max, 4th Max: The four highest “daily max values” in parts per million by volume (ppm). Take the highest 1-hour value of each day, and pick the top four of those values.
• PM2.5 98th %ile: For PM2.5, the 98th percentile of the daily average measurements in the year. (Note: PM2.5 data reported as parameter 88502 are not included in this summary report. Only federal reference method PM2.5 data reported as parameter 88101 are included in this report.)
• PM2.5 Wtd Mean: For PM2.5, the Weighted Annual Mean (mean weighted by calendar quarter) for the year. (Note: PM2.5 data reported as parameter 88502 are not included in this summary report. Only federal reference method PM2.5 data reported as parameter 88101 are included in this report.)
• PM10 24-hr 2nd Max: For PM10, the 2nd highest 24-hour average measurement in the year.
• PM10 Annual Mean: For PM10, the Weighted Annual Mean (mean weighted by calendar quarter) for the year.
• CO 1-hr 2nd Max: For Carbon Monoxide, the 2nd highest 1-hour measurement in the year.
• CO 8-hr 2nd Max: For Carbon Monoxide, the 2nd highest non-overlapping 8-hour average in the year.
• NO2 98th %ile: For Nitrogen Dioxide, the 98th percentile of the daily max 1-hour measurements in the year.
• NO2 Annual Mean: For Nitrogen Dioxide, the annual mean of all the 1-hour measurements in the year.
Image: Outdoor Air Quality Sensors
Outdoor Air Quality Data, Sensor Variables
## [1] "CO_2M_1h" "CO_2M_8h" "County"
## [4] "NO2_98thP_1h" "NO2_Mean_1h" "Oz_2M_1h"
## [7] "Oz_5M_8h" "PM10_2M_24h" "PM10_Mn_24h"
## [10] "PM2.5_98P_24h" "PM2.5_WM_24h" "Year"Outdoor Air Quality Sensor Summary Statistics
## Year County CO_2M_1h
## Min. :2012 Length:202 Min. : 0.60
## 1st Qu.:2014 Class :character 1st Qu.: 1.70
## Median :2016 Mode :character Median : 2.20
## Mean :2016 Mean : 2.84
## 3rd Qu.:2019 3rd Qu.: 2.88
## Max. :2021 Max. :24.10
## CO_2M_8h NO2_98thP_1h NO2_Mean_1h
## Min. :0.50 Min. :17.0 Min. : 2.0
## 1st Qu.:1.10 1st Qu.:37.0 1st Qu.: 8.0
## Median :1.50 Median :47.0 Median :11.0
## Mean :1.71 Mean :47.2 Mean :12.2
## 3rd Qu.:1.90 3rd Qu.:56.0 3rd Qu.:15.0
## Max. :6.70 Max. :96.0 Max. :32.0
## Oz_2M_1h Oz_5M_8h PM2.5_98P_24h
## Min. :0.0500 Min. :0.0430 Min. : 8
## 1st Qu.:0.0800 1st Qu.:0.0640 1st Qu.: 24
## Median :0.1000 Median :0.0755 Median : 35
## Mean :0.0973 Mean :0.0762 Mean : 39
## 3rd Qu.:0.1100 3rd Qu.:0.0850 3rd Qu.: 47
## Max. :0.1700 Max. :0.1250 Max. :144
## PM2.5_WM_24h PM10_2M_24h PM10_Mn_24h
## Min. : 3.10 Min. : 21 Min. :11.0
## 1st Qu.: 8.72 1st Qu.: 51 1st Qu.:19.0
## Median :11.00 Median : 87 Median :25.5
## Mean :11.46 Mean :117 Mean :29.2
## 3rd Qu.:13.68 3rd Qu.:154 3rd Qu.:36.0
## Max. :22.80 Max. :465 Max. :65.0Air Quality Sensor Data Exploration
The next step of this process included reviewing the entirety of the data to determine the strongest correlated relationships between the air quality sensors. The data was standardized which contained 202 counties that contained ten air quality sensors.
Applying the Pearson Correlation Coefficient Analysis The Pearson correlation coefficient (PCC) is also known as Pearson’s r, the Pearson product-moment correlation coefficient (PPMCC), bivariate correlation, or simply as the correlation coefficient. The Pearson Correlation Coefficient formula used to understand strong and weak relationships to measure the standard deviation, which is a normalized measurement of the covariance value between +1 (indicating a strong relationship), and -1 (indicating a weak relationship). It provides the ability to measure the linear correlations between two sets of data, and is the ratio between the covariance of two variables and the product of their standard deviations. The simplified formula is as follows:
\[Pearson Correlation Coefficient_{xy} = \frac {n({\sum}_{xy})-({\sum}_{x})({\sum}_{y})}{\sqrt(n\sum_{x^2}-(\sum_{x^2}))(n\sum_{y^2}-(\sum_{y^2}))}\] Correlation is measured by \(r\), where \(n\) is the sample size, and \(x\) and \(y\) are the sample means of x and y values.
Correlated Air Quality Sensor Data Values
options(width = 60)
aqdata.cor = cor(AQSD[3:12])
corplot <- round(aqdata.cor,2)
corplot## CO_2M_1h CO_2M_8h NO2_98thP_1h NO2_Mean_1h
## CO_2M_1h 1.00 0.82 0.27 0.24
## CO_2M_8h 0.82 1.00 0.44 0.33
## NO2_98thP_1h 0.27 0.44 1.00 0.87
## NO2_Mean_1h 0.24 0.33 0.87 1.00
## Oz_2M_1h 0.23 0.33 0.66 0.73
## Oz_5M_8h 0.23 0.31 0.64 0.71
## PM2.5_98P_24h 0.04 0.16 0.21 0.17
## PM2.5_WM_24h 0.14 0.27 0.53 0.54
## PM10_2M_24h 0.28 0.40 0.24 0.21
## PM10_Mn_24h 0.19 0.31 0.41 0.39
## Oz_2M_1h Oz_5M_8h PM2.5_98P_24h PM2.5_WM_24h
## CO_2M_1h 0.23 0.23 0.04 0.14
## CO_2M_8h 0.33 0.31 0.16 0.27
## NO2_98thP_1h 0.66 0.64 0.21 0.53
## NO2_Mean_1h 0.73 0.71 0.17 0.54
## Oz_2M_1h 1.00 0.95 0.33 0.67
## Oz_5M_8h 0.95 1.00 0.34 0.69
## PM2.5_98P_24h 0.33 0.34 1.00 0.76
## PM2.5_WM_24h 0.67 0.69 0.76 1.00
## PM10_2M_24h 0.44 0.44 0.40 0.48
## PM10_Mn_24h 0.63 0.66 0.46 0.68
## PM10_2M_24h PM10_Mn_24h
## CO_2M_1h 0.28 0.19
## CO_2M_8h 0.40 0.31
## NO2_98thP_1h 0.24 0.41
## NO2_Mean_1h 0.21 0.39
## Oz_2M_1h 0.44 0.63
## Oz_5M_8h 0.44 0.66
## PM2.5_98P_24h 0.40 0.46
## PM2.5_WM_24h 0.48 0.68
## PM10_2M_24h 1.00 0.80
## PM10_Mn_24h 0.80 1.00Visualization the Correlation of Air Quality Sensor Relationships The values shown within the section along with the color scheme provide the ability to understand the information more clearly. The values closer to 1 show stronger relationships.
library(corrplot)
aqdata.cor = cor(AQSD[3:12])
corrplot(aqdata.cor, method="number", number.cex = .5, tl.cex = .5)
Analyzing the Air Quality Sensor Data Values Reviewing
this correlation matrix (shown in two separate visualizations with the
same data), we can determine the most significant relationships between
the air quality sensors. For example:
- Common measurements of CO, NO2, Ozone, and PPM values have more significant relationships to their own measurements than to other, however,
- NO2 and Ozone have strong relationships near 0.7
- It is evident that PM2.5_WM_24th has higher correlated relationships to the other factors than the other PPM measurements
This information is scientifically significant, however the intention is to determine how these air quality values are associated with populations and healthcare.
Healthcare data
Within the State of California, the Office of Statewide Health Planning and Development requires hospitals and healthcare facilities to provide an annual utilization report. This report provides an extensive amount of information about the statistics of providers and patients within their facilities and is publicly available and can be downloaded from the sourced website.
This data has an average of 500 facilities (in which the quantity fluctuates annually) with approximately 275 categories that are quantitatively measured each year, amounting to roughly 140,000 data points each year that are collected by the state.
The initial step with the healthcare data was to standardize and determine variations with population data. Although the State of California has 58 counties, the healthcare data only covers 56 counties. Furthermore, each county contained one or multiple hospitals. The healthcare data was standardized to include the county name and a summation of the values within the county. These PIVOT tables were combined into a final spreadsheet including each year, county, as well as the type and quantity of healthcare services that were provided, amounting to over 13,000 data points for each year.
The second phase of standardization included comparing the row labels, which include information about the healthcare facilities, and the pertinent information we are seeking which includes over 240 healthcare data characteristics such as emergency room visits, licensed bed days, and many others for each county. The datasets were collected and labeled similarly between 2012-2017, 2018-2019, and 2020 in separate groups.
Healthcare Utilization Dataset List of Variables
options(width = 60)
# Hospital Utilization, sample from 2020
HUsamp <- read.table("03-HC/2020_hosp_util_dataonly.csv", header=TRUE, sep=",")
ls(HUsamp[1:5]) # Facility information## [1] "FAC_CITY" "FAC_NAME" "FAC_NO"
## [4] "FAC_STR_ADDR" "OSHPD_ID"ls(HUsamp[200:210]) # EMS Sample types of data## [1] "ADMITTED_FROM_ed_TOT"
## [2] "EMS_VISITS_CRITICAL_ADMITTED"
## [3] "EMS_VISITS_MODERATE_ADMITTED"
## [4] "EMS_VISITS_NON_URGENT_ADMITTED"
## [5] "EMS_VISITS_SEVERE_ADMITTED"
## [6] "EMS_VISITS_URGENT_ADMITTED"
## [7] "ER_TRAFFIC_TOT"
## [8] "INPAT_OPER_RM"
## [9] "INPATIENT_SURG_OPER"
## [10] "OUTPAT_OPER_RM"
## [11] "OUTPATIENT_SURG_OPER"Comparing Healthcare, Air Quality, and Population Data
The next objective with the healthcare data is to determine the statistical significance between healthcare and air quality data. It is important to understand that “correlation does not imply causation”, meaning that certain factors of air quality data are not the only reason that someone may visit a healthcare facility for a specific reason. However, the statistical relationships can provide the ability to forecast future utilization for some factors.
This included analyzing healthcare data between 2012-2017 (then later adding additional years with specific features), along with the outdoor air quality between the same years. The ability to determine the Pearson correlation coefficient of this data provides the ability to examine and recognize relationships between healthcare, air quality, and population.
options(width = 60)
# Hospital Utilization, sample from 2020
AQHCPOP <- read.table("05-Correlation/2020AQHCPOP.csv", header=TRUE, sep=",")
ls(AQHCPOP)## [1] "CO.2nd.Max.1.hr"
## [2] "CO.2nd.Max.8.hr"
## [3] "County"
## [4] "NO2.98th.Percentile.1.hr"
## [5] "NO2.Mean.1.hr"
## [6] "Ozone.2nd.Max.1.hr"
## [7] "Ozone.4th.Max.8.hr"
## [8] "PM10.2nd.Max.24.hr"
## [9] "PM10.Mean.24.hr"
## [10] "PM2.5.98th.Percentile.24.hr"
## [11] "PM2.5.Weighted.Mean.24.hr"
## [12] "Population"
## [13] "Sum.of.ED_TRAFFIC_TOTL"
## [14] "Sum.of.EMS_NON_EMERG_VIS"
## [15] "Sum.of.EMS_REGISTERS_NO_TREAT"
## [16] "Sum.of.HOSP_LICBED_DAY_TOTL"
## [17] "Year"Data Summary:
options(width = 60)
summary(AQHCPOP)## Year County CO.2nd.Max.1.hr
## Min. :2012 Length:164 Min. : 0.60
## 1st Qu.:2014 Class :character 1st Qu.: 1.70
## Median :2016 Mode :character Median : 2.20
## Mean :2016 Mean : 2.86
## 3rd Qu.:2017 3rd Qu.: 2.80
## Max. :2019 Max. :24.10
## CO.2nd.Max.8.hr NO2.98th.Percentile.1.hr NO2.Mean.1.hr
## Min. :0.50 Min. :17.0 Min. : 2.0
## 1st Qu.:1.00 1st Qu.:38.0 1st Qu.: 8.0
## Median :1.50 Median :48.0 Median :11.0
## Mean :1.72 Mean :47.9 Mean :12.2
## 3rd Qu.:1.90 3rd Qu.:57.2 3rd Qu.:15.0
## Max. :6.70 Max. :96.0 Max. :32.0
## Ozone.2nd.Max.1.hr Ozone.4th.Max.8.hr
## Min. :0.0500 Min. :0.0430
## 1st Qu.:0.0800 1st Qu.:0.0640
## Median :0.0950 Median :0.0750
## Mean :0.0955 Mean :0.0755
## 3rd Qu.:0.1100 3rd Qu.:0.0850
## Max. :0.1600 Max. :0.1160
## PM2.5.98th.Percentile.24.hr PM2.5.Weighted.Mean.24.hr
## Min. : 8.0 Min. : 3.10
## 1st Qu.: 23.0 1st Qu.: 8.57
## Median : 32.0 Median :10.80
## Mean : 36.1 Mean :11.15
## 3rd Qu.: 43.0 3rd Qu.:13.40
## Max. :100.0 Max. :22.80
## PM10.2nd.Max.24.hr PM10.Mean.24.hr Population
## Min. : 21 Min. :11.0 Min. : 19377
## 1st Qu.: 50 1st Qu.:18.0 1st Qu.: 424506
## Median : 76 Median :25.0 Median : 834224
## Mean :109 Mean :28.3 Mean : 1434900
## 3rd Qu.:123 3rd Qu.:35.0 3rd Qu.: 1919177
## Max. :465 Max. :65.0 Max. :10105708
## Sum.of.HOSP_LICBED_DAY_TOTL Sum.of.ED_TRAFFIC_TOTL
## Min. : 16790 Min. : 0
## 1st Qu.: 284297 1st Qu.: 153223
## Median : 668192 Median : 299904
## Mean : 1344352 Mean : 495538
## 3rd Qu.: 1435373 3rd Qu.: 593590
## Max. :10834949 Max. :3823574
## Sum.of.EMS_NON_EMERG_VIS Sum.of.EMS_REGISTERS_NO_TREAT
## Min. : 0 Min. : 0
## 1st Qu.: 302 1st Qu.: 3080
## Median : 9806 Median : 7238
## Mean : 22863 Mean : 12379
## 3rd Qu.: 28166 3rd Qu.: 13194
## Max. :253781 Max. :112769Healthcare, Air Quality, and Population Relationships: The previous dataset is shown in this correlation plot.
AQHCPOPdata.cor = cor(AQHCPOP[3:17])
corrplot(AQHCPOPdata.cor, method="number", number.cex = .5, tl.cex = .5)
# FinalCorrelation <- corrplot.mixed(AQHCPOPdata.cor, lower.col = "darkblue", order="hclust", number.cex = .7, tl.cex = .7, tl.col = "black")This data was outputted into another program, analyzed, and shown in the following image.
Post processed image of Correlation Data
The correlation matrix indicates relationships between the variables which are measured by the Euclidean distance. The Euclidean distance is a value between zero (0) and one (1), and the higher the value the more significant the relationship between the values.
This analysis has shown that the higher presence of NO2 and Ozone, followed closely by CO have higher correlated values to licensed bed total days, emergency room traffic, as well as healthcare registrations without treatment.
Data Standardization and Final Forecasts The next step of this process included reviewing the entirety of the data to determine the maximum amount of values that were in common and occurred the most frequently for mathematical and statistical purposes. The detailed process entailed:
- Compiling healthcare utilization, air quality, and population data files
- Initially omitting air quality entries that did not entail all data categories
- Correlating the large dataset to determine the highest values
- Isolating the values of interest, which includes healthcare
utilization metrics:
- Sum of HOSP_LICBED_DAY_TOTL
- Sum of ED_TRAFFIC_TOTL
- Sum of EMS_NON_EMERG_VIS
- Sum of EMS_REGISTERS_NO_TREAT
- Revisiting the air quality dataset, to include all counties with air quality values that matched the highest correlation value
- Correlate the new combined dataset
This diagram shows the process, which includes over 120 data objects
for over 110 counties and years, amounting to over 13,000 data points
for determining the strongest relationships using the this correlation
method. The following steps will show how this data was processed.
Results and Conclusion
The scientific research performed with this data utilized several different techniques while applying analytics, data science, statistics and mathematical processes. The software platforms enabled the ability to manage, comprehend, and visualize the data using multiple platforms.
The topics of healthcare utilization, poor air quality, and the impact on human health are indicative of negative concerns for society. However, the ability to understand this information through data enables us to prepare for current and future challenges to appropriately respond.
This type of research provides many exciting opportunities to consider while improving human health, healthcare systems, and our environment that we live within.
This data was additionally represented in ESRI’s Geographical Information System platform to visualize the data, which may be accessed at https://arcg.is/19iviv.
References and Works Cited
- State boundaries: 2018 United States 1:5,000,000 Cartographic Boundary Source: U.S Department of Commerce, U.S Census Bureau, Geographic Customer Services Branch Projection: North American Datum (NAD) 1983 Release Date: 2018 URL: census.gov/geographies/mapping-files/time-series/geo/carto-boundary-file.html
- Series Information for County Subdivision State-based (California counties), TIGER/Line Shapefile, 2016 Source: U.S. Department of Commerce, U.S. Census Bureau, Geography Division, Spatial Data Collection and Products Branch Projection: North American Datum (NAD) 1983 Release Date: 2016 URL: census.gov/geographies/mapping-files/time-series/geo/tiger-geodatabase-file.html
- Annual Estimates of the Resident Population: April 1, 2010 to July 1, 2019 (PEPANNRES). Source: U.S. Census Bureau, Population Division Release Date: December 2019 URL: census.gov/data/tables/time-series/demo/popest/2010s-counties-total.html
- Air Quality Statistics Report, Geographic Area: California, Summary: by County Source: U.S. Environmental Protection Agency (EPA) AirData Release date (last known from data): May 2021 URL: epa.gov/outdoor-air-quality-data/air-quality-statistics-report
- Air Quality Monitor Listing (with site locations) Source: U.S. Environmental Protection Agency (EPA) AirData Updated annually URL: aqs.epa.gov/aqsweb/airdata/download_files.html#Meta
- Annual Utilization Report of Hospitals Source: Office of Statewide Health Planning and Development (OSHPD) Released annually with different reporting intervals URL: data.chhs.ca.gov/dataset/hospital-annual-utilization-report
*Data source URLS are subject to change depending upon the access date of accessing URL
Keywords Air quality, air quality sensors, California, cartography, county, analytics, data analysis, data relationships, data science, data sets, data visualization, emergency response, environment, geography, geographic information systems (GIS), healthcare data, healthcare utilization, Office of Statewide Health Planning and Development (OSHPD), pearson correlation coefficient, population, preparedness, sensors, spatial data, statistics, U.S. Census Bureau, U.S. Department of Commerce, U.S. Environmental Protection Agency.
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Adapter’s License means the license You apply to Your Copyright and Similar Rights in Your contributions to Adapted Material in accordance with the terms and conditions of this Public License.
BY-NC-SA Compatible License means a license listed at creativecommons.org/compatiblelicenses, approved by Creative Commons as essentially the equivalent of this Public License.
Copyright and Similar Rights means copyright and/or similar rights closely related to copyright including, without limitation, performance, broadcast, sound recording, and Sui Generis Database Rights, without regard to how the rights are labeled or categorized. For purposes of this Public License, the rights specified in Section 2(b)(1)-(2) are not Copyright and Similar Rights.
Effective Technological Measures means those measures that, in the absence of proper authority, may not be circumvented under laws fulfilling obligations under Article 11 of the WIPO Copyright Treaty adopted on December 20, 1996, and/or similar international agreements.
Exceptions and Limitations means fair use, fair dealing, and/or any other exception or limitation to Copyright and Similar Rights that applies to Your use of the Licensed Material.
License Elements means the license attributes listed in the name of a Creative Commons Public License. The License Elements of this Public License are Attribution, NonCommercial, and ShareAlike.
Licensed Material means the artistic or literary work, database, other material to which the Licensor applied this Public License.
Licensed Rights means the rights granted to You subject to the terms and conditions of this Public License, which are limited to all Copyright and Similar Rights that apply to Your use of the Licensed Material and that the Licensor has authority to license.
Licensor means the individual(s) or entity(ies) granting rights under this Public License.
NonCommercial means not primarily intended for or directed towards commercial advantage or monetary compensation. For purposes of this Public License, the exchange of the Licensed Material for other material subject to Copyright and Similar Rights by digital file-sharing or similar means is NonCommercial provided there is no payment of monetary compensation in connection with the exchange.
Share means to provide material to the public by any means or process that requires permission under the Licensed Rights, such as reproduction, public display, public performance, distribution, dissemination, communication, or importation, and to make material available to the public including in ways that members of the may access the material from a place and at a time individually chosen by them.
Sui Generis Database Rights means rights other than copyright resulting from Directive 96/9/EC of the European Parliament and of the Council of 11 March 1996 on the legal protection of databases, as amended and/or succeeded, as well as other essentially equivalent rights anywhere in the world.
You means the individual or entity exercising the Licensed Rights under this Public License. Your has a corresponding meaning.
Section 2 – Scope.
License grant.
Subject to the terms and conditions of this Public License, Licensor hereby grants You a worldwide, royalty-free, non-sublicensable, non-exclusive, irrevocable license to exercise the Licensed Rights in the Licensed Material to:
reproduce and Share the Licensed Material, in whole or in part, for NonCommercial purposes only; and
produce, reproduce, and Share Adapted Material for NonCommercial purposes only.
Exceptions and Limitations. For the avoidance of doubt, where Exceptions and Limitations apply to Your use, this Public License does not apply, and You do not need to comply with its terms and conditions.
Term. The term of this Public License is specified in Section 6(a).
Media and formats; technical modifications allowed. The Licensor authorizes You to exercise the Licensed Rights in all media and formats whether now known or hereafter created, to make technical modifications necessary to do so. The Licensor waives and/or agrees not to assert any right or authority to forbid You from making technical modifications necessary to exercise the Licensed Rights, including technical modifications necessary to circumvent Effective Technological Measures. For purposes of this Public License, making modifications authorized by this Section 2(a) (4) never produces Adapted Material.
Downstream recipients.
Offer from the Licensor – Licensed Material. Every recipient of the Licensed Material automatically receives an offer from the Licensor to exercise the Licensed Rights under the terms and conditions of this Public License.
Additional offer from the Licensor – Adapted Material. Every recipient of Adapted Material from You automatically receives an offer from the Licensor to exercise the Licensed Rights in the Adapted Material under the conditions of the Adapter’s License You apply.
No downstream restrictions. You may not offer or impose any additional or different terms or conditions on, or apply any Effective Technological Measures to, the Licensed Material if doing so restricts exercise of the Licensed Rights by any recipient of the Licensed Material.
No endorsement. Nothing in this Public License constitutes or may be construed as permission to assert or imply that You are, or that Your use of the Licensed Material is, connected with, or sponsored, endorsed, or granted official status by, Licensor or others designated to receive attribution as provided in Section 3(a)(1)(A)(i).
Other rights.
Moral rights, such as the right of integrity, are not licensed under this Public License, nor are publicity, privacy, and/or other similar personality rights; however, to the extent possible, the Licensor waives and/or agrees not to assert any such rights held by the Licensor to the limited extent necessary to allow You to exercise the Licensed Rights, but not otherwise.
Patent and trademark rights are not licensed under this Public License.
To the extent possible, the Licensor waives any right to collect royalties from You for the exercise of the Licensed Rights, whether directly or through a collecting society under any voluntary or waivable statutory or compulsory licensing scheme. In all other cases the Licensor expressly reserves any right to collect such royalties, including when the Licensed Material is used other than for NonCommercial purposes.
Section 3 – License Conditions.
Your exercise of the Licensed Rights is expressly made subject to the following conditions.
Attribution.
If You Share the Licensed Material (including in modified form), You must:
retain the following if it is supplied by the Licensor with the Licensed Material:
identification of the creator(s) of the Licensed Material and any others designated to receive attribution, in any reasonable manner requested by the Licensor (including by pseudonym if designated);
a copyright notice;
a notice that refers to this Public License;
a notice that refers to the disclaimer of warranties;
a URI or hyperlink to the Licensed Material to the extent reasonably practicable;
indicate if You modified the Licensed Material and retain an indication of any previous modifications; and
indicate the Licensed Material is licensed under this Public License, and include the text of, or the URI or hyperlink to, this Public License.
You may satisfy the conditions in Section 3(a)(1) in any reasonable manner based on the medium, means, and context in which You Share the Licensed Material. For example, it may be reasonable to satisfy the conditions by providing a URI or hyperlink to a resource that includes the required information.
If requested by the Licensor, You must remove any of the information required by Section 3(a)(1)(A) to the extent reasonably practicable.
ShareAlike.
In addition to the conditions in Section 3(a), if You Share Adapted Material You produce, the following conditions also apply.
The Adapter’s License You apply must be a Creative Commons license with the same License Elements, this version or later, or a BY-NC-SA Compatible License.
You must include the text of, or the URI or hyperlink to, the Adapter’s License You apply. You may satisfy this condition in any reasonable manner based on the medium, means, and context in which You Share Adapted Material.
You may not offer or impose any additional or different terms conditions on, or apply any Effective Technological Measures to, Adapted Material that restrict exercise of the rights granted under the Adapter’s License You apply.
Section 4 – Sui Generis Database Rights.
Where the Licensed Rights include Sui Generis Database Rights that apply to Your use of the Licensed Material:
for the avoidance of doubt, Section 2(a)(1) grants You the right to extract, reuse, reproduce, and Share all or a substantial portion of the contents of the database for NonCommercial purposes only;
if You include all or a substantial portion of the database contents in a database in which You have Sui Generis Database Rights, then the database in which You have Sui Generis Database Rights (but not its individual contents) is Adapted Material, including for purposes of Section 3(b); and
You must comply with the conditions in Section 3(a) if You Share or a substantial portion of the contents of the database.
For the avoidance of doubt, this Section 4 supplements and does not replace Your obligations under this Public License where the Licensed Rights include other Copyright and Similar Rights.
Section 5 – Disclaimer of Warranties and Limitation of Liability.
UNLESS OTHERWISE SEPARATELY UNDERTAKEN BY THE LICENSOR, TO THE EXTENT POSSIBLE, THE LICENSOR OFFERS THE LICENSED MATERIAL AS-IS AND AS-AVAILABLE, AND MAKES NO REPRESENTATIONS OR WARRANTIES OF ANY KIND CONCERNING THE LICENSED MATERIAL, WHETHER EXPRESS, IMPLIED, STATUTORY, OR OTHER. THIS INCLUDES, WITHOUT LIMITATION, WARRANTIES OF TITLE, MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE, NON-INFRINGEMENT, ABSENCE OF LATENT OR OTHER DEFECTS, ACCURACY, OR THE PRESENCE OR ABSENCE OF ERRORS, WHETHER OR NOT KNOWN OR DISCOVERABLE. WHERE DISCLAIMERS OF WARRANTIES ARE NOT ALLOWED IN FULL OR IN PART, THIS DISCLAIMER MAY NOT APPLY TO YOU.
TO THE EXTENT POSSIBLE, IN NO EVENT WILL THE LICENSOR BE LIABLE TO YOU ON ANY LEGAL THEORY (INCLUDING, WITHOUT LIMITATION, NEGLIGENCE) OR OTHERWISE FOR ANY DIRECT, SPECIAL, INDIRECT, INCIDENTAL, CONSEQUENTIAL, PUNITIVE, EXEMPLARY, OR OTHER LOSSES, COSTS, EXPENSES, OR DAMAGES ARISING OUT OF THIS PUBLIC LICENSE OR USE OF THE LICENSED MATERIAL, EVEN IF THE LICENSOR HAS BEEN ADVISED OF THE POSSIBILITY OF SUCH LOSSES, COSTS, EXPENSES, OR DAMAGES. WHERE A LIMITATION OF LIABILITY IS NOT ALLOWED IN FULL OR IN PART, THIS LIMITATION MAY NOT APPLY TO YOU.
The disclaimer of warranties and limitation of liability provided above shall be interpreted in a manner that, to the extent possible, most closely approximates an absolute disclaimer and waiver of all liability.
Section 6 – Term and Termination.
This Public License applies for the term of the Copyright and Similar Rights licensed here. However, if You fail to comply with this Public License, then Your rights under this Public License terminate automatically.
Where Your right to use the Licensed Material has terminated under Section 6(a), it reinstates:
automatically as of the date the violation is cured, provided it is cured within 30 days of Your discovery of the violation; or
upon express reinstatement by the Licensor.
For the avoidance of doubt, this Section 6(b) does not affect any right the Licensor may have to seek remedies for Your violations of this Public License.
For the avoidance of doubt, the Licensor may also offer the Licensed Material under separate terms or conditions or stop distributing the Licensed Material at any time; however, doing so will not terminate this Public License.
Sections 1, 5, 6, 7, and 8 survive termination of this Public License.
Section 7 – Other Terms and Conditions.
The Licensor shall not be bound by any additional or different terms or conditions communicated by You unless expressly agreed.
Any arrangements, understandings, or agreements regarding the Licensed Material not stated herein are separate from and independent of the terms and conditions of this Public License.
Section 8 – Interpretation.
For the avoidance of doubt, this Public License does not, and shall not be interpreted to, reduce, limit, restrict, or impose conditions on any use of the Licensed Material that could lawfully be made without permission under this Public License.
To the extent possible, if any provision of this Public License is deemed unenforceable, it shall be automatically reformed to the minimum extent necessary to make it enforceable. If the provision cannot be reformed, it shall be severed from this Public License without affecting the enforceability of the remaining terms and conditions.
No term or condition of this Public License will be waived and no failure to comply consented to unless expressly agreed to by the Licensor.
Nothing in this Public License constitutes or may be interpreted as a limitation upon, or waiver of, any privileges and immunities that apply to the Licensor or You, including from the legal processes of any jurisdiction or authority.
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