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3.3: Organizing Resources

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    Organizing systems arrange resources according to many different principles. In libraries, museums, businesses, government agencies and other long-lived institutions, organizing principles are typically documented as cataloging rules, information management policies, or other explicit and systematic procedures so that different people can apply them consistently over time.In contrast, the principles for arranging resources in personal or small-scale organizing systems are usually informal and often inconsistent or conflicting.

    For most types of resources, any number of principles could be used as the basis for their organization depending on the answers to the “why?(“Why Is It Being Organized?”), “how much?(“How Much Is It Being Organized?”), and “how?(“How (or by Whom) Is It Organized?”) questions posed in Design Decisions in Organizing Systems.

    A simple principle for organizing resources is colocation putting all the resources in the same location: in the same container, on the same shelf, or in the same email in-box.However, most organizing systems use principles that are based on specific resource properties or properties derived from the collection as a whole. What properties are significant and how to think about them depends on the number of resources being organized, the purposes for which they are being organized, and on the experiences and implicit or explicit biases of the intended users of the organizing system. The implementation of the organizing system also shapes the need for, and the nature of, the resource properties.[2]

    Many resource collections acquire resources one at a time or in sets of related resources that can initially be treated the same way. Therefore, it is natural to arrange resources based on properties that can be assessed and interpreted when the resource becomes part of the collection.

    Subject matter” organization involves the use of a classification system that provides categories and descriptive terms for indicating what a resource is about. Because they use aboutness properties that are not directly perceived, methods for assigning subject classifications are intellectually-intensive and in many cases require rigorous training to be performed consistently and appropriately.[3] Nevertheless, the cost and time required for this human effort motivates the use of computational techniques for organizing resources.

    As computing power steadily increases, the bias toward computational organization gets even stronger. However, an important concern arises when computational methods for organizing resources use so-called “black box” methods that create resource descriptions and organizing principles that are not inspectable or interpretable by people. In some applications more efficient information retrieval or question answering, more accurate predictions, or more personalized recommendations justify making the tradeoff. But comprehensibility is critical in many medical, military, financial, or scientific applications, where trusting a prediction can have life or death implications or cause substantial time or money to be spent.[4]

    Organizing Physical Resources

    When the resources being arranged are physical or tangible thingssuch as books, paintings, animals, or cooking potsany resource can be in only one place at a time in libraries, museums, zoos, or kitchens. Similarly, when organizing involves recording information in a physical mediumcarving in stone, imprinting in clay, applying ink to paper by hand or with a printing presshow this information can be organized is subject to the intrinsic properties and constraints of physical things.

    The inescapable tangibility of physical resources means that their organizing systems are often strongly influenced by the material or medium in which the resources are presented or represented. For example, museums generally collect original artifacts and their collections are commonly organized according to the type of thing being collected. There are art museums, sculpture museums, craft museums, toy museums, science museums, and so on.

    Similarly, because they have different material manifestations, we usually organize our printed books in a different location than our record albums, which might be near but remain separate from our CDs and DVDs. This is partly because the storage environments for physical resources (shelves, cabinets, closets, and so on) have co-evolved with the physical resources they store.[5]

    The resource collections of organizing systems in physical environments often grow to fit the size of the environment or place in which they are maintainedthe bookshelf, closet, warehouse, library or museum building. Their scale can be large: the Smithsonian Institute in Washington, D.C., the world’s largest museum and research complex, consists of 19 museums, 9 research facilities, a zoo and a library with 1.5 million books. However, at some point, any physical space gets too crowded, and it is difficult and expensive to add new floors or galleries to an existing library or museum.

    Organizing with Properties of Physical Resources

    Physical resources are often organized according to intrinsic physical properties like their size, color, or shape, because the human visual system automatically pays a lot of attention to them.

    This inescapable aspect of visual perception was first formalized by German psychologists starting a century ago as the Gestalt principles (see the sidebar, Gestalt Principles). Likewise, because people have limited attentional capacity, we ignore a lot of the ongoing complexity of visual (and auditory) stimulation, making us perceive our sensory world as simpler than it really is. Taken together, these two ideas explain why we automatically or “pre-attentively” organize separate things we see as groups or patterns based on their proximity and similarity. They also explain why arranging physical resources using these quickly perceived attributes can seem more aesthetic or satisfying than organizing them using properties that take more time to understand. Look at the cover of this book; the most organized arrangement of the colors and shapes just jumps out at you more than the others.

    Physical resources are also commonly organized using intrinsically associated properties such as the place and time they were created or discovered. The shirts in your clothes closet might be arranged by color, by fabric, or style. We can view dress shirts, T-shirts, Hawaiian shirts and other styles as configurations of shirt properties that are so frequent and familiar that they have become linguistic and cultural categories. Other people might think about these same properties or categories differently, using a greater or lesser number of colors or ordering them differently, sorting the shirts by style first and then by color, or vice versa.

    In addition to, or instead of, physical properties of your shirts, you might employ behavioral or usage-based properties to arrange them. You might separate your party and Hawaiian shirts from those you wear to the office. You might put the shirts you wear most often in the front of the closet so they are easy to locate. Unlike intrinsic properties of resources, which do not change, behavioral or usage-based properties are dynamic. You might move to Hawaii, where you can wear Hawaiian shirts to the office, or you could get tired of what were once your favorite shirts and stop wearing them as often as you used to.

    Some arrangements of physical resources are constrained or precluded by resource properties that might cause problems for other resources or for their users. Hazardous or flammable materials should not be stored where they might spill or ignite; lions and antelopes should not share the same zoo habitat or the former will eat the latter; adult books and movies should not be kept in a library where children might accidentally find them; and people who are confrontational, passive aggressive, or arrogant do not make good team members when tough decisions need to be made. For almost any resource, it seems possible to imagine a combination with another resource that might have unfortunate consequences. We have no shortage of professional certifications, building codes, MPAA movie ratings, and other types of laws and regulations designed to keep us safe from potentially dangerous resources.

    Organizing with Descriptions of Physical Resources

    To overcome the inherent constraints with organizing physical resources, organizing systems often use additional physical resources that describe the primary physical ones, with the library card catalog being the classic example. A specific physical resource might be in a particular place, but multiple description resources for it can be in many different places at the same time.

    When the description resources are themselves digital, as when a printed library card catalog is put online, the additional layer of abstraction created enables additional organizing possibilities that can ignore physical properties of resources and many of the details about how they are stored.

    In organizing systems that use additional resources to identify or describe primary ones, “adding to a collection” is a logical act that need not require any actual movement, copying, or reorganization of the primary resources. This virtual addition allows the same resources to be part of many collections at the same time; the same book can be listed in many bibliographies, the same web page can be in many lists of web bookmarks and have incoming links from many different pages, and a publisher’s digital article repository can be licensed to any number of libraries.

    Organizing Places

    Places are physical resources, but unlike the previous two subsections where we treat the environment as given (the library or museum building, the card catalog or bookshelf) and discuss how we organize resources like books in that environment, we can take an alternative perspective and discuss how we design that physical environment. These environments could be any of the following:

    • The land itself, as when we lay out city plans when organizing how people live together and interact in cities.
    • A “built environment,” a human-made space, particular building, or a set of connected spaces and buildings. A built environment could be a museum, airport, hospital, casino, department store, farm, road system, or any kind of building or space where resources are arranged and people interact with them.
    • The orientation and navigation aids that enable users to understand and interact in built environments. These are resource descriptions that support the interaction requirements of the users.

    These are not entirely separable contexts, but they are easier to discuss as if they are considered as such.

    Organizing the Land

    Cities naturally emerge in places that can support life and commerce. Almost all major cities are built on coasts or rivers because water provides sustenance, transportation and commercial links, and power to enable industry. Many very old cities have crowded and convoluted street plans that do not seem intentionally organized, but grid plans in cities also have a very long history. Cities in the Middle East were laid out in rough grids as far back as 2000+ BCE. Using long axes was a way to create an impression of importance and power.

    Because the United States, and especially the American West, was not heavily settled until much more recently compared to most of Europe and Asia, it was a place for people to experiment with new ideas in urban design. The natural human tendency to impose order on habitation location had ample room to do just that. The easiest and most efficient way to organize space is using a coordinate grid, with streets intersecting at perpendicular angles. Salt Lake City, Albuquerque, Phoenix, and Seattle are notable examples of grid cities. An interesting hybrid structure exists in Washington DC, which has radiating diagonal avenues overlaid on a grid.[9]

    Organizing Built Environments

    Built environments influence the expectations, behaviors, and experiences of everyone who enters the space—employees, visitors, customers, and inhabitants are all subject to the design of the spaces they occupy. These environments can be designed to encourage or discourage interactions between people, to create a sense of freedom or confinement, to reward exploration or enforce efficiency, and of course, much much more. The arrangement of the resources in a built environment also encourages or discourages interactions, and sometimes the built environment is designed with a specific collection of resources in mind to enable and reinforce some particular interaction goals or policies.

    If we contrast the built environments of museums, airports, and casinos, and the way in which each of them facilitates or constrains interactions are more obvious. Museums are often housed in buildings designed as architectural monuments that over time become symbols of national, civic, or cultural identity. Many old art museums mimic classical architecture, with grand stairs flanked by tall columns. They have large and dramatic entry halls that invite visitors inside. Modern museums are decidedly less traditional, and some people complain that the architecture of modern art museums can overshadow the art collection contained within because people are induced to pay more attention to the building than to its contents.

    Some recently built airports have been designed with architectural flair, but airport design is more concerned with efficiency, walkability (maybe with the aid of moving walkways), navigability, and basic comfort for travelers getting in and out the airport. Wide walkways, multiple staircases, and people movers whose doors open in one direction at a time, all encourage people to move in certain directions, sometimes without the people even realizing they are being directed.

    If you have ever been lost in a casino or had trouble finding the exit you can be sure you experienced a casino that achieved its main design goals: keeping people inside and making it easy for them to lose track of time because they lack both windows and clocks. As American architect Robert Venturi points out, “The intricate maze under the low ceiling never connects with outside light or outside space…This disorients the occupant in space and time… He loses track of where he is and when it is.[10]

    If one accepts the premise that values and bias are at work in decisions about organizing systems, it is difficult not to see it in built environments. Consider queue design in banks, supermarkets, or boarding airplanes. Assuming that it is desirable to organize people efficiently to minimize wait times and crowding, how should the queue be designed? How many categories of people should there be? What is the basis for the categories?

    It may be uncontroversial to include several express lanes in a supermarket checkout, because people can choose to buy fewer items if they do not want to wait. Similarly, it seems essential for hospital emergency rooms to have a triage policy that selects patients from the emergency room queue based on their likely benefit from immediate medical attention.

    However, consider the dynamic created by queue design at Disneyland to give priority to people with physical limitations and disabilities. This seemingly socially respectful decision was exploited by a devious collaboration between disabled people and wealthy non-disabled people who hired them to pose as family members, enabling the entire “family” to cut ahead of everyone else. In response, Disney modified the policy favoring disabled patrons, causing numerous complaints about Disney’s insensitivity to their concerns.[11]

    There are many other examples of how values and biases become part of built environments. In the mid-20th century the road systems of Long Island in New York were designed with low overpasses, which prevented public buses from passing under them, effectively segregating the beaches. The trend in college campus design after the student protests of the 1960s and 1970s was to create layouts that would prevent or frustrate large demonstrations.[12]

    Orientation and Wayfinding Mechanisms

    It is easy to move through an environment and stay oriented if the design is simple and consistent, but most built environments must include additional features or descriptions to assist people in these tasks. Distinctive architectural elements can create landmarks for orientation, and spaces can be differentiated with color, lighting, furnishings, or other means. More ubiquitous mechanisms include signs, room numbers, or directional arrows highlighting the way and distance to important destinations.

    In airports, for example, there are many orientation signs and display terminals that help passengers find their departure gates, baggage, or ground transportation services. In contrast, casinos provide little orientation and navigation support because increased confusion leads to lengthier visits, and more gambling on the part of the casino’s visitors.

    A recent innovation in wayfinding and orientation mechanisms is to give them sensing and communication capabilities so they can identify people by their smartphones and then provide personalized directions or information. These so-called “beacon” systems have been deployed at numerous airports, including London’s Gatwick, San Francisco, and Miami. [13]

    Organizing Digital Resources

    Organizing systems that arrange digital resources like digital documents or information services have some important differences from those that organize physical resources. Because digital resources can be easily copied or interlinked, they are free from the “one place at a time” limitation.[14] The actual storage locations for digital resources are no longer visible or very important. It hardly matters if a digital document or video resides on a computer in Berkeley or Bangalore if it can be located and accessed efficiently.[15]

    Moreover, because the functions and capabilities of digital resources are not directly manifested as physical properties, the constraints imposed on all material objects do not matter to digital content in many circumstances.[16]

    An organizing system for digital resources can also use digital description resources that are associated with them. Since the incremental costs of adding processing and storage capacity to digital organizing systems are small, collections of both primary digital resources and description resources can be arbitrarily large. Digital organizing systems can support collections and interactions at a scale that is impossible in organizing systems that are entirely physical, and they can implement services and functions that exploit the exponentially growing processing, storage and communication capabilities available today. This all sounds good, unless you are the small local business with limited onsite inventory that cannot compete with global web retailers that offer many more choices from a network of warehouses.[18]

    There are inherently more arrangements of digital resources than there are for physical ones, but this difference emerges because of multiple implementation platforms for the organizing system as much as in the nature of the resources. Nevertheless, the organizing systems for digital books, music and video collections often maintain the distinctions embodied in the organizing system for physical resources because it enables their co-existence or simply because of legacy inertia. As a result, the organizing systems for collections of digital resources tend to be coarsely distinguished by media type (e.g., document management, digital music collection, digital video collection, digital photo collection, etc.).

    Information resources in either physical or digital form are typically organized using intrinsic properties like author names, creation dates, publisher, or the set of words that they contain. Information resources can also be organized using assigned properties like subject classifications, names, or identifiers. Information resources can also be organized using behavioral or transactional properties collected about individuals or about groups of people with similar interaction histories. For example, Amazon and Netflix use browsing and purchasing behavior to make book and movie recommendations.[19]

    Complex organization and interactions are possible when organizing systems with digital resources are based on the data type or data model of the digital content (e.g., text, numeric, multimedia, statistical, geospatial, logical, scientific, personnel, and so on).

    Interactions with numeric data can be further distinguished according to the levels of measurement embodied in the number, which determine how much quantitative processing makes sense:

    • Nominal level data uses a number as an identifier for an instance or a category to distinguish it from other ones. Products in a catalog might have numbers associated with them, but the products have no intrinsic order, so no measurements using the numbers are meaningful other than the frequency with which they occur in the dataset. The most frequently occurring value is called the mode.
    • Ordinal level data indicates a direction or ranking on some naturally ordered scale. We know that the first place finisher in a race came in ahead of the second place one, who finished ahead of the third place finisher, but this result conveys no information about the spacing among the racers at the finish line. The middle value in a sorted list is the median.
    • Interval level data conveys order information, but in addition, the values that subdivide the scale are equally spaced. This makes it meaningful to calculate the distance between values, the mean or average value (the value for which the sum of its absolute distances to each other value is zero), the standard deviation, and other descriptive statistics about the data.
    • Ratio level data is interval data with a fixed zero point, which makes assertions about proportions meaningful. $10,000 is twice as much as $5,000.

    These distinctions are data type and levels of measurement are often strongly identifiable with business functions: operational, transactional, process control, and predictive analytics activities require the most fine-grained data and quantitative measurement scales, while strategic functions might rely on more qualitative analyses represented in narrative text formats.

    Just as there are many laws and regulations that restrict the organization of physical resources, there are laws and regulations that constrain the arrangements of digital ones. Many information systems that generate or collect transactional data are prohibited from sharing any records that identify specific people.Banking, accounting, and legal organizing systems are made more homogeneous by compliance and reporting standards and rules.

    Organizing Web-based Resources

    The Domain Name System(DNS) is the most inherent scheme for organizing web resources. Top-level domains for countries (.us, .jp, .cn, etc.) and generic resource categories (.com, .edu. .org, gov, etc.) provide some clues about the resources organized by a website. These clues are most reliable for large established enterprises and publishers; we know what to expect at ibm.com, Berkeley.edu, and sfgov.org.[21]

    The network of hyperlinks among web resources challenges the notion of a collection, because it makes it impractical to define a precise boundary around any collection smaller than the complete web. [22]

    Furthermore, authors are increasingly using “web-native” publication models, creating networks of articles that blur the notions of articles and journals. For example, scientific authors are interconnecting scientific findings with their underlying research data, to discipline-specific data repositories, or to software for analyzing, visualizing, simulation, or otherwise interacting with the information.[23]

    The conventional library is both a collection of books and the physical space in which the collection is managed. On the web, rich hyper linking and the fact that the actual storage location of web resources is unimportant to the end users fundamentally undermine the idea that organizing systems must collect resources and then arrange them under local control to be effective. The spectacular rise during the 1990s of the AOLwalled garden,” created on the assumption that the open web was unreliable, insecure, and pernicious, was for a time a striking historical reminder and warning to designers of closed resource collections until its equally spectacular collapse in the following decade.[24] But Facebook so far is succeeding by following a walled garden strategy.

    “Information Architecture” and Organizing Systems

    The discipline known as information architecture can be viewed as a specialized approach for designing the information models and their systematic manifestations in user experiences on websites and in other information-intensive organizing systems. [25] Abstract patterns of information content or organization are sometimes called architectures, so it is straightforward from the perspective of the discipline of organizing to define the activity of information architecture as designing an abstract and effective organization of information and then exposing that organization to facilitate navigation and information use. Note how the first part of this definition refers to intentional arrangement of resources, and the second to the interactions enabled by that arrangement.

    Our definition of information architecture implies a methodology for the design of user interfaces and interactions that puts conceptual modeling at the foundation. Best practices in information architecture emphasize the use of systematic principles or design patterns for organizing the resources and interactions in user interfaces. The logical design is then translated into a graphical design that arranges windows, panes, menus, and other user interface components. The logical and graphical organization of a user interface together affect how people interact with it and the actions they take (or do not take).

    Some information design conventions have become design patterns. Documents use headings, boxes, white space, and horizontal rules to organize information by type and category. Large type signifies more important content than small type, red type indicates an advisory or warning, and italics or bold says “pay attention.

    Some patterns are general and apply to an entire website, page, or interface genre such as a government site, e-commerce site, blog, social network site, home page, “about us” page, and so on. Other patterns are more specific and affect a part of a site or a single component of a page (e.g., autocompletion of a text field, breadcrumb menu, slideshow).

    In websites, different categories of content or interactions are typically arranged in different menus. The choices within each menu are then arranged to reflect typical workflows or ordered according to some commonly used property like size, percentage, or price.

    All design patterns reflect and reinforce the user’s past experiences with content and interface components, and this familiarity reduces the cognitive complexity of user interface interaction, requiring users to pay less attention.[27]

    However, interface designers can take advantage of this familiarity and employ design patterns in a less beneficial way to manipulate users, control their behaviors, or trick them into taking actions they do not intend. Patterns used this way are sometimes called Dark Patterns.

    Many organizing systems need to support interactions to find, identify, and select resources. Some of these systems contain both physical and digital resources, as in a bookstore with both web and physical channels, and many interactions are implemented across more than one device. Both the cross-channel and multiple-device situations create user expectations that interactions will be consistent across these different contexts. Starting with a conceptual model and separating content and structure from presentation, as we discussed in “The Concept of “Organizing Principle””, gives organizing systems more implementation alternatives and makes them more robust in the face of technology diversity and change.

    A model-based foundation is also essential in information visualization applications, which depict the structure and relationships in large data collections using spatial and graphical conventions to enable user interactions for exploration and analysis. By transforming data and applying color, texture, density, and other properties that are more directly perceptible, information visualization applications enable people to obtain more information than they can from text displays. [29]

    Some designers of information systems put less emphasis on conceptual modeling as an “inside-out” foundation for interaction design and more emphasis on an “outside-in” approach that highlights layout and other presentation-tier considerations with the goal of making interactions easy and enjoyable. This focus is typically called user experience design, and information architecture methods remain an important part of it, but not beginning with explicit organizing principles implies more heuristic methods and yields less predictable results.

    Organizing With Descriptive Statistics

    Descriptive statistics, about a collection or dataset, summarize it concisely and can identify the properties that might be most useful as organizing principles. The simplest statistical description of a collection is how big it is; how many resources or observations does it contain?

    Descriptive statistics summarize a collection of resources or dataset with two types of measures:

    • Measures of central tendency: Mean, median, and mode; which measure is appropriate depends on the level of measurement represented in the numbers being described (these measures and the concept of levels of measurements are defined in “Organizing Digital Resources”).
    • Measures of variability: Range (the difference between the maximum and minimum values), and standard deviation (a measure of the spread of values around the mean).

    Statistical descriptions can be created for any resource property, with the simplest being the number of resources that have the property or some particular value of it, such as the number of times a particular word occurs in a document or the number of copies a book has sold. Comparing summary statistics about a collection with the values for individual resources helps you understand how typical or representative that resource is. If you can compare your height of 6 feet, ½ inch with that of the average adult male, which is 5 feet, 10 inches, the difference is two and a half inches, but what does this mean? It is more informative to make this comparison using the standard deviation, which is three inches, because this tells you that 68% of adult men have heights between 5 feet, 7 inches and 6 feet, 1 inch. When measurements are normally distributed in the familiar bell-shaped curve around the mean, the standard deviation makes it easy to identify statistical outliers.

    No matter how measurements are distributed, it can be useful to employ descriptive statistics to organize resources or observations into categories or quantiles that have the same number of them. Quartiles (4 categories), deciles (10), and percentiles (100) are commonly used partitions.

    Alternatively, resources or observations can be organized by visualizing them in a histogram, which divides the range of values into units with equal intervals. Because values tend to vary around some central tendency, the intervals are unlikely to contain the same number of observations. Descriptive statistics and associated visualizations can suggest which properties make good organizing principles because they exhibit enough variation to distinguish resources in their most useful interactions. For example, it probably isn’t useful to organize books according to their weight because almost all books weigh between ½ and 2 pounds, unless you are in the business of shipping books and paying according to how much they weigh.

    Exploratory Analysis to Understand Data

    Many experts recommend that data analysts should undertake some exploratory analysis with descriptive statistics and simple information visualizations to understand their data before applying sophisticated computational techniques to the dataset. In particular, because the human visual system quickly perceives shapes and patterns, analyzing and graphing the values of data attributes and other resource descriptions can suggest which properties might be useful and comprehensible organizing principles. In addition, data visualization makes it easy to recognize values that are typical or that are outliers. Some of this analysis might form part of data quality assessment during resource selection, but if not done then, it should be done as part of the organizing process.

    A dataset whose fields or attributes lack information about data types and units of measure has little use because the data lacks meaning. When some, but not all parts of the data are named or annotated, avoid over-interpreting these descriptions’ meanings. (See “Naming Resources”.)

    We will do some exploratory analysis to understand what an example dataset contains and how we might use it. For our example, we consider a collection of a few hundred records from a healthcare study, whose first eight records and first five data fields in each record are shown in Figure: Example Dataset.

    Example Dataset
    ID Sex Temp Age Weight
    1 1 97.6 32 135
    2 0 97.6 19 118
    3 0 97.6 23 128
    4 1 98.7 34 140
    5 1 98.5 52 162
    6 1 98.7 60 160
    7 0 98.3 36 148
    8 0 98.3 38 155
    260 1 99.0 23 123

    The “ID” column contains numeric data, but every value is a different integer, and the values are contiguous. The field label “ID” suggests that this is the resource identifier for the participants in the healthcare study. Further examination of other tables will reveal that this is a key value that points into a different dataset containing the resource names.

    The “Sex” column is also numeric, but there are only two different values, 0 and 1, and in the complete dataset they are approximately equal in frequency. This attribute seems to be categorical or Boolean data. This makes sense for a “Sex” categorization, and it is likely to prove useful in understanding the dataset.

    The “Temp” column contains several hundred different numeric values in the complete dataset, ranging from 96.8 to 100.6, with a mean of 98.6. These values are sensible if the label “Temp” means the under-the-tongue body temperature in degrees Fahrenheit of the study participant when the other measures were obtained. This type of data is usefully viewed as a histogram to get a sense of the spread and shape, shown in Figure: Temperature.

    A histogram presenting distribution ranging from 96.8 to 100.6 degrees Fahrenheit. A bulge or bell shape occurs between 98.2 and 99.4 degrees, with a peak at 98.7 degrees.

    The data values of the “Temp” column follow the familiar normal or bell-shaped distribution, for which simple and useful descriptive statistics are the mean and the standard deviation. The mean (or average) is at the center of the distribution, and the standard deviation captures the width of the bell shape. In this dataset, the very narrow range of data values here suggests that this attribute is not useful as an organizing principle, since it does not distinguish the resources in any significant way. In a larger sample, however, there might be a few very low or very high temperatures, and it would be useful to investigate these “hypothermic” or “hyperthermic” outliers.

    The data values of the “Age” column range from 18 to 97, and are spread broadly across the entire range; this is the age, in years, of the study participants. When a distribution is very broad and flat, or highly skewed with many values at one end or another, the mean value is less useful as a descriptive statistic. Instead of the mean, it is better to use the median or middle value as a summary of the data; the median value for “Age” in the complete dataset is 39.

    Age
    A histogram presenting the distribution of ages of test subject. Peaks occur from 20–40 years, 50–55 years, 60–65 years, followed by a steady decline toward 100 years, except for a notable bump at 85–90 years old.

    The “Weight” column has about 220 different numeric values, from 82 to 300, and judging from this range we can infer that the weights are measured in pounds. The data follows an uneven distribution with peaks around 160 and 200, and a small peak at 300. This odd shape appears in the histogram of Figure: Weight. The two peaks in this so-called multi-modal histogram suggest that this measure is mixing two different kinds of resources, and indeed it is because weights of men and women follow different distributions. It would thus be useful to use the categorical “Sex” data to separate these populations, and Figure: Sex and Weight: Female shows how analyzing weight for women and men as different populations is much more informative as an organizing principle than combining them.

    What about the odd peak in the distribution at 300? End of range anomalies like this generally reflect a limitation in the device or system that created the data. In this case, the weight scale must have an upper limit of 300 pounds, so the peak represents the people whose weight is 300 or greater.

    Weight
    A histogram presenting weight distribution among the test subjects. There are two noticeable bumps in the number of subjects whose weights were recorded at 150–165 pounds, and again at 180–195 pounds. There are an equal number of subjects in the 75–105 pound range and in the 300–315 pound range.
    Sex and Weight: Female
    A histogram presenting weight distribution among the female test subjects. There is a noticeable bumps in the number of subjects whose weights were recorded at 140–160 pounds. There are an equal number of female subjects in the 75–105 pound range and in the 255–300 pound range.
    Sex and Weight: Male
    A histogram presenting weight distribution among the male test subjects. There is a noticeable upward curve that begins at 150 pounds, peaks at 180–195 pounds, drops off noticeably above 210 pounds, again above 240 pounds. There is one less male subject in the 105–135 range than in the 255–315 range.

    Detecting Errors and Fraud in Data

    There are numerous techniques for evaluating individual data items or datasets to ensure that they have not been changed or corrupted during transmission, storage, or copying. These include parity bits, check digits, check sums, and cryptographic hash functions. They share the idea that a calculation will yield some particular value or match a stored result when the original data has not been changed. Another basic technique for detecting errors is to look for data values that are different or anomalous because they do not fall into expected ranges or categories.

    More interesting challenges arise when the data might have been changed by intentional actions to commit fraud, launder money, or carry out some other crime. In these situations, the person tampering with data or creating fake data will try to make the data look normal or expected.

    Forensic accountants and statisticians use many techniques for detecting possibly fraudulent data in these adversarial contexts. Some are quite simple:

    • If expenses are reimbursed up to some maximum allowed value, look for data items with that exact value.
    • When any value exceeding some threshold triggers more careful analysis, look for other data items just below that threshold.
    • When invoices or claims are paid on receipt, and only a sample are subsequently audited, look for duplicate submissions.
    • Calculate the ratio of the maximum to the minimum value for purchases in some category (such as the unit price paid for items from suppliers); items with large ratios might indicate fraud where the supplier “kicks back” some of the money to the purchaser.

    Benford’s Law, the observation that the leading digits in data sets are distributed in a non-uniform manner, is an effective technique for detecting fraudulent data because it is based on a counter-intuitive fact not known to most fraudsters, who often make up data to look random. You might think that the number 1 would occur 11% of the time as the first digit (since there are 9 possibilities), but for data sets whose values span several orders of magnitude, the number 1 is the first digit about 30% of the time, and 7, 8, and 9 occur around 5%.

    Because of the very high transaction rate and the relatively small probability of fraud, credit card fraud is detected using machine learning algorithms. The classifier is trained with known good and bad transactions using properties like average amount, frequency, and location to develop a model of each cardholder’s “data behavior” so that a transaction can quickly be assigned a probability that it is fraudulent. (More about this kind of computational classification in Categorization: Describing Resource Classes and Types.)[31]

    Organizing with Multiple Resource Properties

    Multiple properties of the resources, the person organizing or intending to use them, and the social and technological environment in which they are being organized can collectively shape their organization. For example, the way you organize your home kitchen is influenced by the physical layout of counters, cabinets, and drawers; the dishes you cook most often; your skills as a cook, which may influence the number of cookbooks, specialized appliances and tools you own and how you use them; the sizes and shapes of the packages in the pantry and refrigerator; and even your height.

    If multiple resource properties are considered in a fixed order, the resulting arrangement forms a logical hierarchy.The top level categories of resources are created based on the values of the property evaluated first, and then each category is further subdivided using other properties until each resource is classified in only a single category. Consider the hierarchical system of folders used by a professor to arrange the digital resources on his computer; the first level distinguishes personal documents from work-related documents; work is then subdivided into teaching and research, teaching is subdivided by year, and year divided by course.

    For physical resources, mapping categories to physical locations is another required step; for example, resources in the “kitchen utensils” category might all be arranged in drawers near a workspace, with “silverware” arranged more precisely to separate knives, forks, and spoons.

    An alternative to hierarchical organization that is often used in digital organizing systems is faceted classification, in which the different properties for the resources can be evaluated in any order. For example, you can select wines from the wine.com store catalog by type of grape, cost, or region and consider these property facets in any order. Three people might each end up choosing the same moderately-priced Kendall Jackson California Chardonnay, but one of them might have started the search based on price, one based on the grape varietal, and the third with the region. This kind of interaction in effect generates a different logical hierarchy for every different combination of property values, and each user made his final selection from a different set of wines.

    Faceted classification allows a collection of description resources to be dynamically re-organized into as many categories as there are combinations of values on the descriptive facets, depending on the priority or point of view the user applies to the facets. Of course this only works because the physical resources are not themselves being rearranged, only their digital descriptions.

    Applications that organize large collections of digital information, including those for search, natural language processing, image classification, personalized recommendation, and other computationally intensive domains, often use huge numbers of resource properties (which are often called “features” or “dimensions”). For example, in document collections each unique word might initially be treated as a feature by machine learning algorithms, so there might be tens of thousands of features.

    Classification: Assigning Resources to Categories explains principles and methods for hierarchical and faceted classification in more detail.


    1. See (Barsalou and Hale 1983) for a rigorous contrast between feature lists and other representational formalisms in models of human categories.


    2. For example, a personal or small organizing system would typically use properties that are easy to identify and understand. In contrast, an organizing system for very large collections of resources, or data about them, would choose properties that are statistically optimal, even if they are not interpretable by people, because of the greater need for operational efficiency and predictive accuracy.


    3. Libraries and bookstores use different classification systems. The kitchen in a restaurant is not organized like a home kitchen because professional cooks think of cooking differently than ordinary people do.Scientists use the Latin or binomial (genus + species) scheme for identifying and classifying living things to avoid the ambiguities and inconsistencies of common names, which differ across languages and often within different regions in a single language community.


    4. (Freitas 2014) and (Burrell 2015).


    5. Many of the ancient libraries in Greece and Rome have been identified by archaeologists by characteristic architectural features (Casson 2002). See also (Battles 2003).


    6. (Robertson 2015) and (Coase 1937).


    7. The Gestalt principles are a staple in every introductory psychology textbook, but the classic text (Koffka 1935) has recently been reprinted. A group of distinguished contemporary researchers in visual perception (Wagemans et al, 2012) recently reviewed the history and impact of Gestalt psychology on their hundredth birthday.


    8. Texts that ground graphic design and information visualization in Gestalt principles include (Cairo 2012) and (Few 2004). (Johnson 2013) explains them within the broader scope of user interface design.


    9. Salt Lake City takes the use of a grid to an extreme because the central area is extremely flat. Streets are named by numbers and letters, so you might find yourself at the intersection of “North A Street” and “3rd Avenue N,” or at the intersection of “W 100 S” and “S 200 W.” It is a little creepy to think that your street address is a pinpoint location in the big grid.

      In contrast, Seattle imposes the grid in an abstract way, ignoring the fact that there are many lakes, rivers, and hills that break up the grid. Streets keep the same names even though they are not connected, and the grid stretches for many miles out from its origin in Seattle. You can be up in the mountains at the corner of “294th Avenue SE” and “472nd Street SE,” giving you precise information about your location and nearly 50 mile distance from downtown Seattle.

      (See also Pierre Charles L'Enfant's plan for DC at http://en.Wikipedia.org/wiki/Pierre_Charles_L%27Enfant)

      This is not to say that imposing arbitrary grids on top of a physical environment to create a simple and easily understood organization is always desirable. It is essential that any organization imposed on a region be sensitive to any social, cultural, linguistic, ethnic, or religious organizing systems already in place. Much of the recent conflict and instability in the Middle East can be attributed to the implausibly straight line borders drawn by the French and British to carve up the defeated Ottoman Empire a century ago. Because the newly-created countries of Syria and Iraq lacked ethnic and religious cohesion, they could only be held together by dictatorships. (Trofimov 2015)


    10. (Shiner 2007). The comparison of the organizing systems in casinos and airports comes from (Curran 2011). (Venturi 1972)


    11. The number of queues, their locations and their layout (if spatial) is referred to as the “queue configuration.” The “queue discipline” is the policy for selecting the next customer from the queue Most common discipline is “First come, first served.” Frequent, higher-paying, or some other customer segment might have their own queue with FCFS applied within it.

      See the New York Post article at http://nypost.com/2013/05/14/rich-manhattan-moms-hire-handicapped-tour-guides-so-kids-can-cut-lines-at-disney-world/


    12. The designer of the road system, Robert Moses, heralded as the master builder of mid-20th century New York City, built roads to enforce his idea of who should frequent Long Island (affluent whites). The overpasses were intentionally designed with clearances (often around nine feet) that were too low for public buses. Consequently, low-income bus riders (largely people of color) had no way to get to beaches. See (Winner 1980).


    13. (Arthur and Passini 1992)(McCartney 2015)McCartney, Scott. Technology will speed you through the airport of the future. Wall Street Journal, July 15 2015.


    14. In principle, it is easy to make perfect copies of digital resources. In practice, however, many industries employ a wide range of technologies including digital rights management, watermarking, and license servers to prevent copying of documents, music or video files, and other digital resources. The degree of copying allowed in digital organizing systems is a design choice that is shaped by law.


    15. Web-based or “cloud” services are invoked through (Berners-Lee 1998). Digital resources are often replicated in content delivery networks to improve performance, reliability, scalability, and security (Pathan et al. 2008); the web pages served by a busy site might actually be delivered from different parts of the world, depending on where the accessing user is located.


    16. Whether a digital resource seems intangible or tangible depends on the scale of the digital collection and whether we focus on individual resources or the entire collection. An email message is an identified digital resource in a standard format, RFC 2822(Resnick 2001). We can compare different email systems according to the kinds of interactions they support and how easy it is to carry them out, but how email resources are represented does not matter to us and they surely seem intangible. Similarly, the organizing system we use to manage email might employ a complex hierarchy of folders or just a single searchable in-box, but whether that organization is implemented in the computer or smart phone we use for email or exists somewhere “in the cloud” for web-based email does not much matter to us either. An email message is tangible when we print it on paper, but all that matters then is that there is well-defined mapping between the different representations of the abstract email resource.

      On the other hand, at the scale at which Google and Microsoft handle billions of email messages in their Gmail and Hotmail services the implementation of the email organizing system is extremely relevant and involves many tangible considerations. The location and design of data centers, the configuration of processors and storage devices, the network capacity for delivering messages, whether messages and folder structures are server or client based, and numerous other considerations contribute to the quality of service that we experience when we interact with the email organizing system.


    17. (Schreibman, Siemens, and Unsworth 2005) and (Leonardi 2010). For example, a “Born-Digital Archives” program at Emory University is preserving a collection of the author Salman Rushdie’s work that includes his four personal computers and an external hard drive. (Kirschenbaum 2008), and (Kirschenbaum et al. 2009).


    18. For example, a car dealer might be able to keep track of a few dozen new and used cars on his lot even without a computerized inventory system, but web-based AutoTrader.com offered more than 2,000,000 cars in 2012. The cars are physical resources where they are located in the world, but they are represented in the AutoTrader.com organizing system as digital resources, and cars can be searched for using any combination of the many resource properties in the car listings: price, body style, make, model, year, mileage, color, location, and even specific car features like sunroofs or heated seats.


    19. Even when organizing principles such as alphabetical, chronological, or numerical ordering do not explicitly consider physical properties, how the resources are arranged in the “storage tier” of the organizing system can still be constrained by their physical properties and by the physical characteristics of the environments in which they are arranged. Books can only be stacked so high whether they are arranged alphabetically or by frequency of use, and large picture books often end up on the taller bottom shelf of bookcases because that is the only shelf they fit. Nevertheless, it is important to treat these idiosyncratic outcomes in physical storage as exceptions and not let them distort the choice of the organizing principles in the “logic tier.


    20. (Spence 1985) This memory technique has continued to be used since, and in addition to being found in tips for studying and public speaking, is applied in memorization competitions. For example, journalist and authorJoshua Foer, in his book on memory and his journey from beginner to winning the 2006 U.S. Memory Championship (Foer 2011), wrote that Scott Hagwood, a four-time winner of the same competition, used locations in Architectural Digest to place his memories.


    21. The Domain Name System(DNS)(Mockapetris 1987) is the hierarchical naming system that enables the assignment of meaningful domain names to groups of Internet resources. The responsibility for assigning names is delegated in a distributed way by the Internet Corporation for Assigned Names and Numbers(ICANN) (www.icann.org). DNS is an essential part of the Web’s organizing system but predates it by almost twenty years.


    22. HTML5 defines a “manifest” mechanism for making the boundary around a collection of web resources explicit even if somewhat arbitrary to support an “offline” mode of interaction in which all needed resources are continually downloaded (http://www.w3.org/TR/html5/browsers.html#offline), but many people consider it unreliable and subject to strange side effects.


    23. (Aalbersberg and Kahler 2011).


    24. (Munk 2004).


    25. This definition of information architecture combines those in a Wikipedia article (http://en.Wikipedia.org/wiki/Information_architecture) and in a popular book with the words in its title (Morville and Rosenfield 2006). Given the abstract elegance of “information” and “architecture” any definition of “information architecture” can seem a little feeble.

      See (Resmini and Rosati 2011) for a history of information architecture.


    26. See (Halvorson and Rach 2012), (Tidwell 2008), (Morville and Rosenfield 2006), (Kalbach 2007), (Resmini and Rosati 2011), (Marcotte 2011), (Brown 2010), (Abel and Baillie 2014)


    27. Some popular collections of design patterns are (Van Duyne et. al, 2006), (Tidwell 2010), and http://ui-patterns.com/


    28. The Directives can be found at http://ec.europa.eu/consumers/consumer_rights/rights-contracts/directive/index_en.htm


    29. The classic text about information visualization is The Visual Display of Quantitative Information(Tufte 1983). More recent texts include (Few 2012) and (Yau 2011).


    30. (Rose 2016)


    31. See https://chapters.theiia.org/ottawa/D...l_Analysis.pdf for a short introduction to data analysis for fraud detection. See (Durtschi et al 2004) for the use of Benford’s Law in forensic accounting.



    This page titled 3.3: Organizing Resources is shared under a CC BY-NC 4.0 license and was authored, remixed, and/or curated by Robert J. Glushko via source content that was edited to the style and standards of the LibreTexts platform; a detailed edit history is available upon request.

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