"Binning has two basic limitations. First, binning sacrifices resolution. Sometimes plots of the raw data will reveal interesting fine structure that is hidden by binning. However, advantages from binning often outweigh the disadvantage from lost resolution. [...] Second, binning does not extend well to high dimensions. With reasonable univariate resolution, say 50 regions each covering 2% of the range of the variable, the number of cells for a mere 10 variables is exceedingly large. For uniformly distributed data, it would take a huge sample size to fill a respectable fraction of the cells. The message is not so much that binning is bad but that high dimensional space is big. The complement to the curse of dimensionality is the blessing of large samples. Even in two and three dimensions having lots of data can bc very helpful when the observations are noisy and the structure non-trivial." (Daniel B Carr, "Looking at Large Data Sets Using Binned Data Plots", [in "Computing and Graphics in Statistics"] 1991)
"There is an interplay between statistical models and graphics, so it is advantageous to think about models before making a series of plots." (Daniel B Carr, "Looking at Large Data Sets Using Binned Data Plots", [in "Computing and Graphics in Statistics"] 1991)
"Working with binned data directly addresses large data set issues of computation and plotting speed. Almost everything that can bc done with the original data can be done faster with binned data. Further, working with binned data allows image processing algorithms to be adapted and applied to bin cells. Thus tools can bc brought to bare that are not traditionally associated with exploratory data analysis." (Daniel B Carr, "Looking at Large Data Sets Using Binned Data Plots", [in "Computing and Graphics in Statistics"] 1991)
"A scatterplot would show the relationship between [...] two variables in more detail, but would not convey the spatial patterns shown in […] micromap panels. Using conditioning to define a comparative grid of panels, […] changes an investigation from a sequential filtering of one variable at a time to more of a multivariable approach. In this context we can assess functional relationships, densities, or geospatial patterns within panels as well as changes across panels." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"Another method used to simplify the appearance of a graphic is smoothing. A regression line overlaid on a scatterplot is a smooth representation of the relationship between the two graph variables. For time series data, a moving average of the data over time is often used to smooth out the variation over small time steps in order to illustrate the overall trend." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"Designing good visual displays with an easy-to-use interactive system is difficult. The designer’s first attempts will usually fail, so it is critical that proposed systems be tested on at least several sets of typical users. These usability tests help the designer iterate to the best possible system." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"Given the small size of micromaps, the blocks of color on choropleth maps have the advantage of being more visible than if the values were displayed by small symbols or hatch patterns on the map. Using highly saturated colors makes small areas stand out even more. On the other hand, the eye can be drawn to large blocks of color that represent small populations […] A micromap re-design may attempt to mitigate this areal bias by increasing the size of small […] states, but the analyst needs to be aware of this potential problem when using micromaps to communicate to others. The conditioned micromap design can partially address this issue by conditioning on population." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"Hue is the color dimension that is associated with wavelength of light and with names of colors, such as red, yellow, and blue. Most languages around the world include words for black, white, red, green, yellow, blue, brown, pink, purple, orange, and gray. Differences in hue are best used for encoding different attributes, as in a qualitative graph or unordered variables. Different wavelengths have different focal lengths, so what we 'see' is a compromise between the actual and perceived distance to the image. Most people perceive long-wavelength colors, such as red and orange, as being closer to their eyes than short-wavelength colors, such as blue and green." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"In addition to smoothing boundaries, we can smooth the data. The simultaneous smoothing of variation over space, time, or attributes can help us to see the central patterns that would otherwise be hidden by local variation (noise). Local averaging of values usually can provide less biased estimates of spatial and temporal processes, just as the regression line can provide an unbiased estimate of a linear relationship between variables. However, smoothing can actually mask patterns, particularly important outliers, if we smooth over places that are dissimilar in some relevant attribute." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"Micromap graphics differ from most of [other] methodology in two ways. First, by definition, micromaps always include maps among the views of study units. Second, micromaps use different methods to highlight study units. Linked micromaps sort the study units, partition them into small subsets, and systematically highlight these subsets. The conditioned micromaps and many comparative micromaps use a three-class slider to partition." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"Much of a statistician’s training, especially in thinking about patterns, is related to the statistical tasks of describing and comparing distributions and to creating and refining models that describe how variables are related. There is little direct focus on the tasks of pattern identification, distribution comparison, and model building in the web page design and usability literature. Instead, that community is more focused on searching for and filtering information, drilling down to find a specific piece of information and navigation on the web. Nonetheless, good tools for one purpose often can be adapted to another purpose." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"People have different approaches to reasoning about data, depending on their skills and experience, but research has shown that there are commonalities in their processing steps. Some researchers call this sense making. A classical statistical analysis is usually straightforward, consisting of sequential steps of experimental design, the conduct of the experiment, and a statistical summary of results. An exploratory analysis is often interactive and less structured. Usually there is a phase of information gathering and preliminary processing, followed by choice of the representation method that will address the question at hand or questions raised by preliminary graphics." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"[…] perceptual accuracy decreases with distance, so columns that are to be compared should be side by side. Current linked micromap software requires the user." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"Saturation, also referred to as chroma or intensity, measures the purity of the color. A highly saturated color has little or no gray in it, while a highly desaturated color is almost gray, with none of the original color. You may be more familiar with the term shade, which refers to a mix of pigment and black paint, or tint, a mix of pigment and white paint. We only perceive a few different steps of varying saturation, so changing saturation alone is not effective for encoding a quantitative variable. However, the eye is drawn to highly saturated colors, so these can be used to good effect for drawing attention to a part of the visualization. In addition, highly saturated colors stand out more and so can be used as fill colors to improve the visibility of small symbols or areas." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"Scatterplots are the preferred medium for adding smooth curves to show a causal functional relationship or an association […] However, despite the advantage of the scatterplot for seeing some types of patterns, the linked micromap design adds geographic location to the information displayed and so enables searches for geographic patterns that the scatterplot omits." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"Statistical models typically decompose observed values into fit and residuals. Mapping fitted values shows broad patterns that may help us to understand and explain the process that generated the data. Mapping residuals can show us a mixture of noise and anomalies. Sometimes we are more interested in the broad patterns, but at other times we wish to identify the anomalies, e.g., where some corrective action needs to be taken." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"The power of graphics to aid understanding is well recognized, but with power comes the risk of misuse. Some people advocate the restriction of graphs and data to avoid misuse or to avoid drawing attention to problems. As educators we seek to provide both tools and education with the hope that learning will continue. Graphics can be misused, but our position is that people can learn from mistakes. We also believe that when many people can see and share perspectives, we are in a better position to see constructively and shape the world." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
"The use of color is so fundamental in visualization design that its perception requires an in-depth discussion [...]. Using color well is not easy. Color is one of those concepts that everyone thinks they understand, but that is really more complex than it first appears." (Daniel B Carr & Linda W Pickle, "Visualizing Data Patterns with Micromaps", 2010)
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