Difference between revisions of "SOCR MotionCharts CAOzoneData"
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[[Image:SOCR_OzoneData_GeoMap_Dinov_121608_Fig1.png|200px|thumbnail|right| [http://socr.ucla.edu/docs/resources/SOCR_Data/SOCR_OzoneData_GoogleMap.html Ozone Geo-Map] ]] | [[Image:SOCR_OzoneData_GeoMap_Dinov_121608_Fig1.png|200px|thumbnail|right| [http://socr.ucla.edu/docs/resources/SOCR_Data/SOCR_OzoneData_GoogleMap.html Ozone Geo-Map] ]] | ||
Suppose we are asked to analyze a complex dataset that included observational multivariate ozone depletion data. The data included [[SOCR_Data_Dinov_121608_OzoneData |California Ozone measurements from 20 locations between 1980 and 2006]]. The figure on the right illustrates a dynamic interactive map of the geographic locations of the data measurements. This dataset consists of 540 rows and 22 variables. The goals of the study were to identify relationships and associations between the variables and map geographically the significant ozone layer effects. Any such quantitative study requires a preliminary exploratory data analysis. The complexity of the dataset and the intrinsic measurement characteristics of the ozone data demands a new approach to visualization and exploration of these heterogeneous measurements. | Suppose we are asked to analyze a complex dataset that included observational multivariate ozone depletion data. The data included [[SOCR_Data_Dinov_121608_OzoneData |California Ozone measurements from 20 locations between 1980 and 2006]]. The figure on the right illustrates a dynamic interactive map of the geographic locations of the data measurements. This dataset consists of 540 rows and 22 variables. The goals of the study were to identify relationships and associations between the variables and map geographically the significant ozone layer effects. Any such quantitative study requires a preliminary exploratory data analysis. The complexity of the dataset and the intrinsic measurement characteristics of the ozone data demands a new approach to visualization and exploration of these heterogeneous measurements. | ||
| + | |||
| + | ==Case Study== | ||
| + | This Ozone pollution case study addresses the following specific driving environmental questions: | ||
| + | * Temporal changes in California Ozone | ||
| + | * Geographic distribution of of California Ozone pollution | ||
| + | |||
| + | The following chart illustrates the interpretation of the Ozone data in terms of the particulate (particles per million, ppm) recordings, according to the National Oceanic and Atmospheric Administration's (NOAA) Air Quality Index (AQI). | ||
| + | [[Image:SOCR_OzoneData_AQI_Ozone_Chart1.png|300px|thumbnail|right| [http://www.nws.noaa.gov/aq/supplementalpages/aqkey.php Ozone Air Quality Index Map] ]] | ||
| + | |||
| + | ===Temporal changes in California Ozone=== | ||
| + | Winter (January) and Summer (August) Ozone levels (ppm): Notice the annualized increase of the ozone pollution with time (increase of the proportion of hot-colored bubbles with time). Also observe that the ozone polution seems be a more geographically spread out phenomenon in the 2000's, compared to the 1980's -- most of the bubbles cluster together in later years, whereas there were wider geographic-driven fluctuations in the ozone particles in the earlier years. | ||
| + | |||
| + | ===Geographic distribution of of California Ozone pollution=== | ||
| + | Geographic variation of Ozone pollution: This plot shows a bubble for each of the California locations where monthly measurements of Ozone pollution were recorded between 1980-2006. The size of the bubbles reflect the maximum annual pollution and the bubble color indicates the average annual ozone pollution -- hot-colors represent high and cool-colors represent low ozone pollution levels, respectively. | ||
== Description == | == Description == | ||
Revision as of 15:35, 22 March 2010
Contents
SOCR MotionCharts Activities - California Ozone Data Activity
Summary
This activity demonstrates the usage and functionality of SOCR MotionCharts using the SOCR California Ozone dataset.
Goals
The aims of this activity is to:
- demonstrate data import, MotionChart data manipulations and graphical data interpretation
- explore the interactive graphical visualization of real-life multidimensional datasets
- data navigation from different directions (using data mappings).
Background
Suppose we are asked to analyze a complex dataset that included observational multivariate ozone depletion data. The data included California Ozone measurements from 20 locations between 1980 and 2006. The figure on the right illustrates a dynamic interactive map of the geographic locations of the data measurements. This dataset consists of 540 rows and 22 variables. The goals of the study were to identify relationships and associations between the variables and map geographically the significant ozone layer effects. Any such quantitative study requires a preliminary exploratory data analysis. The complexity of the dataset and the intrinsic measurement characteristics of the ozone data demands a new approach to visualization and exploration of these heterogeneous measurements.
Case Study
This Ozone pollution case study addresses the following specific driving environmental questions:
- Temporal changes in California Ozone
- Geographic distribution of of California Ozone pollution
The following chart illustrates the interpretation of the Ozone data in terms of the particulate (particles per million, ppm) recordings, according to the National Oceanic and Atmospheric Administration's (NOAA) Air Quality Index (AQI).
Temporal changes in California Ozone
Winter (January) and Summer (August) Ozone levels (ppm): Notice the annualized increase of the ozone pollution with time (increase of the proportion of hot-colored bubbles with time). Also observe that the ozone polution seems be a more geographically spread out phenomenon in the 2000's, compared to the 1980's -- most of the bubbles cluster together in later years, whereas there were wider geographic-driven fluctuations in the ozone particles in the earlier years.
Geographic distribution of of California Ozone pollution
Geographic variation of Ozone pollution: This plot shows a bubble for each of the California locations where monthly measurements of Ozone pollution were recorded between 1980-2006. The size of the bubbles reflect the maximum annual pollution and the bubble color indicates the average annual ozone pollution -- hot-colors represent high and cool-colors represent low ozone pollution levels, respectively.
Description
In addition to this activity, open 2 more browser tabs - one pointing to the SOCR MotionCharts applet and the other displaying the SOCR California Ozone dataset. The image below shows this setting.
Activity
- Using the mouse, copy the SOCR California Ozone dataset, click on the first cell (top-left) in the DATA tab of the SOCR Motion Charts applet, and paste the data in the spreadsheet.
- Next, you need to map the column-variables to different properties it the SOCR MotionChart. For example, you can us the following mapping:
| Variables | ||||||
|---|---|---|---|---|---|---|
| SOCR MotionChart Property | Key | X-Axis | Y-Axis | Size | Color | Category |
| Data Column Name | Year | Longitude | Latitude | MTH_1 | MTN_7 | Location |
The figures below represent snapshots of the generated dynamic SOCR motion chart. In the real applet, you can play (animate) or scroll (1-year steps) through the years (1980, ..., 2006). Notice the position change between different snapshots of the time slider on the bottom of these figures. Also, mouse-over a blob triggers a dynamic graphical pop-up providing additional information about the data for the specified blob in the chart.
You can also change what variables are mapped to the following SOCR MotionCharts properties:
- Key, X-Axis, Y-Axis, Size, Color and Category.
Data type and format
SOCR Motion Charts currently accepts three types of data: numbers, dates/time, and strings. With these data types, we feel that the application is able to handle the majority of data out here. We use the natural ordering of these types as defined by Java however. While many types of data can be interpreted as a string, it may not make sense to use lexicological ordering on all the different types. When designing SOCR Motion Charts, we took this into consideration and designed the application so that it can easily be extended to provide a greater variety of interpreted types. Thus, a developer should be able to easily provide better type interpretation for particular types of data.
Applications
The SOCR MotionCharts can be used in a variety of applications to visualize dynamic relationships in multidimensional data in up to four dimensions and a fifth temporal component. Its design and implementation allow for extensions allowing and supporting higher dimensions plug-ins. The overall purpose of SOCR MotionCharts is to provide users with a way to visualize the relationships between multiple variables over a period of time in a simple, intuitive and animated fashion.
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