MODULE 5 -UIUX
Information Search and Data
Visualization:
Information visualization is the interactive visual illustrations of
conceptual data that strengthen human understanding. It has
emerged from the research in human-computer interaction and is
applied as a critical component in varied fields. It allows users to
see, discover, and understand huge amounts of information at once.
Information visualization is also an assumption structure, which is
typically
followed
by
formal
examination
such
as
statistical
hypothesis testing.
Advanced Filtering
Following are the advanced filtering procedures −

Filtering with complex Boolean queries
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Automatic filtering
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Dynamic queries
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Faceted metadata search
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Query by example
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Implicit search
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Collaborative filtering
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Multilingual searches
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Visual field specification
Hypertext and Hypermedia
Hypertext can be defined as the text that has references to
hyperlinks with immediate access. Any text that provides a
reference to another text can be understood as two nodes of
information with the reference forming the link. In hypertext, all
the links are active and when clicked, opens something new.
Hypermedia on the other hand, is an information medium that
holds different types of media, such as, video, CD, and so forth, as
well as hyperlinks.
Hence, both hypertext and hypermedia refers to a system of linked
information. A text may refer to links, which may also have visuals
or media. So hypertext can be used as a generic term to denote a
document, which may in fact be distributed across several media.
Object Action Interface Model for Website
Design
Object Action Interface (OAI), can be considered as the next step of
the Graphical User Interface (GUI). This model focusses on the
priority of the object over the actions.
OAI Model
The OAI model allows the user to perform action on the object.
First the object is selected and then the action is performed on the
object. Finally, the outcome is shown to the user. In this model, the
user does not have to worry about the complexity of any
syntactical actions.
The object–action model provides an advantage to the user as they
gain a sense of control due to the direct involvement in the design
process. The computer serves as a medium to signify different tools.
FIVE STAGE SEARCH FRAMEWORK:
The following points are the five-phase frameworks that clarifies
user interfaces for textual search −
Formulation − expressing the search
Initiation of action − launching the search
Review of results − reading messages and outcomes
Refinement − formulating the next step
Use − compiling or disseminating insight.
FORMULATION:-
What is the formulation stage of the information search process?
The fourth stage in the ISP, formulation, is the turning point of the ISP, when
feelings of uncertainty diminish and confidence increases. The task is to form
a focus from the information encountered.
Formulation, when a focused perspective is formed and uncertainty
diminishes as confidence begins to increase.
Initiation of Action:Consumer behaviour is the study of individuals, groups, or organisations and
all the activities associated with the purchase, use and disposal of goods and
services. Consumer behaviour consists of how the consumer's emotions,
attitudes, and preferences affect buying behaviour. Consumer behaviour
emerged in the 1940–1950s as a distinct sub-discipline of marketing, but
has become an interdisciplinary social science that blends elements
from psychology, sociology, social
anthropology, anthropology, ethnography, ethnology, marketing,
and economics (especially behavioural economics).
The study of consumer behaviour formally investigates individual qualities
such as demographics, personality lifestyles, and behavioural variables (such
as usage rates, usage occasion, loyalty, brand advocacy, and willingness to
provide referrals), in an attempt to understand
people's wants and consumption patterns. Consumer behaviour also
investigates on the influences on the consumer, from social groups such as
family, friends, sports, and reference groups, to society in general (brandinfluencers, opinion leaders).
Research has shown that consumer behaviour is difficult to predict, even for
experts in the field; however, new research methods, such
as ethnography, consumer neuroscience, and machine learning[1] are
shedding new light on how consumers make decisions. In addition, customer
relationship management (CRM) databases have become an asset for the
analysis of customer behaviour. The extensive data produced by these
databases enables detailed examination of behavioural factors that contribute
to customer re-purchase intentions, consumer retention, loyalty, and other
behavioural intentions such as the willingness to provide positive referrals,
become brand advocates, or engage in customer citizenship activities.
Databases also assist in market segmentation, especially behavioural
segmentation such as developing loyalty segments, which can be used to
develop tightly targeted customised marketing strategies on a one-to-one
basis..
Review of Results:-
Data visualization is the practice of translating information into a
visual context, such as a map or graph, to make data easier for the
human brain to understand and pull insights from. The main goal of
data visualization is to make it easier to identify patterns, trends and
outliers in large data sets. The term is often used interchangeably
with others, including information graphics, information visualization
and statistical graphics.
Data visualization is one of the steps of the data science process,
which states that after data has been collected, processed and
modeled, it must be visualized for conclusions to be made. Data
visualization is also an element of the broader data presentation
architecture (DPA) discipline, which aims to identify, locate,
manipulate, format and deliver data in the most efficient way possible.
Data visualization is important for almost every career. It can be used
by teachers to display student test results, by computer scientists
exploring advancements in artificial intelligence (AI) or by executives
looking to share information with stakeholders. It also plays an
important role in big data projects. As businesses accumulated
massive collections of data during the early years of the big data
trend, they needed a way to get an overview of their data quickly and
easily. Visualization tools were a natural fit.
Visualization is central to advanced analytics for similar reasons.
When a data scientist is writing advanced predictive analytics or
machine learning (ML) algorithms, it becomes important to visualize
the outputs to monitor results and ensure that models are performing
as intended. This is because visualizations of complex algorithms are
generally easier to interpret than numerical outputs.
Refining data consists of cleansing and shaping it. When you cleanse data,
you fix or remove data that is incorrect, incomplete, improperly formatted, or
duplicated. And when you shape data, you customize it by filtering, sorting,
combining or removing columns, and performing operations.
As you manipulate your data, you build a customized Data Refinery flow that
you can modify in real time and save for future re-use. When you save the
refined data set, you typically load it to a different location than where you
read it from. In this way, your source data can remain untouched by the
refinement process.
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Prerequisites
Refine your data
Data set previews
Data Refinery flows and steps
Prerequisites
Before you can refine data, you’ll need a project.
If you have data in cloud or on-premises data sources, you’ll
need connections to those sources and you’ll need to add data assets from
each connection. If you want to be able to save refined data to cloud or onpremises data sources, create connections for this purpose as well. Source
connections can only be used to read data; target connections can only be
used to load (save) data. When you create a target connection, be sure to use
credentials that have Write permission or you won’t be able to save your Data
Refinery flow output to the target.
REFINEMENT:Refine your data
Access Data Refinery from within a project. Click Add to project, and
then choose DATA REFINERY FLOW. Then select the data you want
to work with.
Alternatively, from the Assets tab of a project page, you can perform
one of the following actions:



Select Refine from the menu of an Avro, CSV, JSON, Parquet,
or text data asset
Click an Avro, CSV, JSON, Parquet, or text data asset to
preview it first and then click the Refine link
If you already have a Data Refinery flow, click New Data
Refinery flow from the Data Refinery flows section and
then select the data that you want to work with.
Tip: If your data doesn’t display in tabular form, specify the format of
your data source. Go to the Data tab. Scroll down to the SOURCE
FILE information at the bottom of the page. Click the Specify data
format icon.
On the Data tab, apply operations to cleanse, shape, and enrich your
data. You can enter R code in the command line and let autocomplete
assist you in getting the correct syntax.
Validate your data throughout the data refinement process.
Use visualizations to gain insights into your data and to uncover
patterns, trends, and correlations within your data.
When you’ve refined the sample data set to suit your needs, click the
Run Data Refinery flow icon in the toolbar to run the Data Refinery flow
on the entire data set.
By default, Data Refinery uses the name of the data source to name
the Data Refinery flow and the target data set. You can change these
names, but you can’t change the project that these data assets belong
to.
In the Data Refinery flow details pane, optionally edit the name
of the Data Refinery flow and enter a description for it. You can
also add a one-time or repeating schedule for the Data Refinery
flow.
In the Data Refinery flow output pane, optionally edit the name
of the target data set and enter a description for it. You can save
the target data set to the project, to a connection, or to a
connected data asset. If you save it to the project, you can save
it as a new data asset (by default) or you can replace an existing
data asset. To save the target data set to a connection or as a
connected data asset or to replace an existing data asset,
use Change Location.
If you select an existing relational database table or view or you
select a connected relational data asset as the target for your
Data Refinery flow output, you have a number of options for
impacting the existing data set.


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
Overwrite - Overwrite the rows in the existing data set
with those in the Data Refinery flow output
Recreate - Delete the rows in the existing data set and
replace them with the rows in the Data Refinery flow
output
Insert - Append all rows of the Data Refinery flow output
to the existing data set
Update - Update rows in the existing data set with the
Data Refinery flow output; don’t insert any new rows
Upsert - Update rows in the existing data set and append
the rest of the Data Refinery flow output to it
For the Update and Upsert options, you’ll need to select the
columns in the output data set to compare to columns in the
existing data set. The output and target data sets must have the
same number of columns, and the columns must have the same
names and data types in both data sets.
Use -Dynamic Queries and Faceted Search :
The query cycle
1.
2.
Send the user's query to the search engine.
Execute the search and retrieve the records that match the query. In
this step, you'll derive the facets from the retrieved records .
3.
Send back the results and facets.
4.
Render the results and facets on screen.
Overview of dynamic faceting
Dynamic faceting displays to the user a different set of
facets depending on the user’s intent. To understand this,
consider an ecommerce music store that sells two
categories of items: CDs and audio equipment. This
business wants to display a set of relevant facets. What
good is it to propose “brand” when the user’s intent is to
find their favorite music? Likewise, what good is it to
propose “musical genre” when the user’s intent is to find
audio equipment? Dynamic faceting ensures that only the
most appropriate facets show up.
Ecommerce businesses with a diversity of products benefit
from displaying different facets depending on the items the
user is searching for:
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

Pharmaceutical companies display different facets
for their medical vs. cosmetic products.
Newspapers display different facets in their
Entertainment and Political sections.
Online marketplaces, like the Amazon example
below, change facet lists as people navigate through
their vast diversity of offerings.
Example use case: ecommerce
marketplaces & dynamic faceting
Amazon uses dynamic faceting for many of its categories.
In the image below, you see two queries: “music” on the
left, “movies” on the right. As you can see, both sides
include the “price” facet, but the musical query includes
“customer reviews”, “artist”, and “musical format”, while the
movie query includes “director”, “video format” and “movie
genre”.
Amazon uses dynamic faceting to create an enhanced
search experience by guiding the user in a smart and
curated way depending on the products they are
searching for.
Let’s see how this is done.
The query cycle and the logic behind
facet search
First, some terminology:

Facet keys are attributes like “color”, “price”,
“shoe_category”, and “sleeves”.

Facet values are the key’s values. For example,
“color” contains “red” and “green”; “sleeves” contain
“short” and “long”.
The dataset
We’ll use a dataset with two kinds of products: shirts and
shoes. The example below contains two typical items. Both
items include the “price”, “color”, and “clothing_type” facets.
However, shirts contain a “sleeves” facet and shoes a
“shoe_category” facet.
The query cycle
A search query follows a 4-part cycle. Here’s an overview.
We’ll give more details and code examples in the section
that follows.
1. Send the user’s query to the search engine.
2. Execute the search and retrieve the records that
match the query. In this step, you’ll derive the facets
from the retrieved records .
3. Send back the results and facets.
4. Render the results and facets onscreen.
As you’ll see, instead of using a pre-defined list of facets,
the logic consists in dynamically generating a new list with
each query. This is possible by doing the following:

On the back end, you’ll extract facets from every set
of query results.

On the front end, you’ll use undefined containerplaceholders instead of pre-defined containers.
The query request: sending the query
with or without a filter
The starting point of the cycle is to send a query and any
facet value the user has selected to filter their results.
Filtering results creates a cohesive result set, which in turn
generates a list of facets relevant to all of the items that
appear in the results. On the other hand, if the user does
not select a facet, the items will be more diverse — and
therefore, the facets might not apply to all products.
However, this is perfectly fine. As you’ll see in the next step,
presenting the top 5 most common facets ensures that
most items will contain these facets.
Now for the code. Here’s how Algolia’s API implements the
query “Get all short-sleeved summer t-shirts”. (Since all
search tools allow filtering, the following code is only one
among many ways to do this).
The query execution: creating the list
of top 5 facets
The dataset we use in this article contains two kinds of
products, each with a set of unique facet attributes. After
executing the query, the search engine extracts every facet
key that shows up in every record, then selects 5 facets
that appear most often.
Why top 5? Because a screen with 5 facets is usually
enough. Ten is an outer limit – any more would be overkill
and create unused clutter.
There are two methods to create a list of facets:
1. A pre-defined list: save a list of facet keys either
directly in the code or in a separate dataset or local
storage.
2. A dynamically generated list: extract a distinct list of
facet keys from the results of the query and put them
in the query response as a separate record.
We’ve chosen to use method 2, but some implementations
use method 1.
Method 1 – Hardcoding the list of facet keys
For this method, you create a fixed set of facets based on
what you already know about your products. This list can
be either hardcoded, or placed in a file or table in a
database that can be manually updated whenever new
products are added or modified.
Whatever the manner of storing the fixed list, the list must
contain the following kind of example:

For every “clothing_type=shirt”, send back the
following facets: “color, price, clothing_type, sleeves,
gender”.

For every “clothing_type=shoe”, send back the
following facets: “color, price, clothing_type,
shoe_category, brand”.
This is not the preferred method because, as with all
hardcoding or semi-hardcoding, it has limited scalability. If
you want to add more relations, for example
“shoe_styles“ = “high-top, leisure, and cross-fit”, you’ll need
to manually add a new line to the list. Manual maintenance
is extra work and prone to error and delay.
The approach we present in the rest of this article (method
2) removes manual maintenance from the process, making
the process deductive and therefore entirely dynamic.
Method 2 – Dynamically generate the list of facets
In this method, we’ll extract the list of facets from the
products themselves.
In a sense, the only difference between the manual and
dynamic approaches is that the generated list of top 5
facets is dynamic. The resulting list itself will be formatted
in the same way.
Here are the steps:
1. Get the query results:
o
Execute the query, find X number of products.
o
Save those records, to be sent back as the
search results.
2. Get the top 5 facet keys and all their values:
o
From the results, collect all facet attributes
(we’ll show below how to identify an attribute
as a facet).
o
Create a record that contains the full list of
extracted facet keys.
o
Determine which facets appear the most often.
Sort the list by the highest number of records.
o
Take only the top 5 from this list. These are the
most common facets.
o
Add the product’s values to their respective key.
Done.
The generated list would be the same as with method 1:

For every “clothing_type=shirt”, send back the
following facets: “color, price, clothing_type, sleeves,
gender”.

For every “clothing_type=shoe”, send back the
following facets: “color, price, clothing_type,
shoe_category, brand”.
Thus, if most products are shirts, we would display
“sleeves” and “gender” as the 4th and 5th facets to display.
If they were shoes, then the 4th and 5th facets would be
“shoe_category” and “brand”.
The query response: sending back the
response
Here we’ll simply send back the search results and the
generated list of 5 facet keys with their respective values.
The response should include whatever the front end needs
to build its search results page. In all facet search query
cycles, the front end needs:
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A list of products with “name”, “description”, “price”,
and “image_url”. (Don’t send image files, as their size
will slow down the overall response time of the
search).
The list of facet key and their values.
A response will also contain additional information
needed for display purposes or business logic. We
do not show those.
Results:
Here is how we would return the set of shirts. All attributes
will be used as information in the search results, except
“objectID”, which will be used to identify a product for
technical reasons (click analytics, detailed page view, or
other reasons).
Facets:
The facet response is a combination of facet keys with their
values. Here’s a small extract. The example does not
include all of the facets:
Two things to note:

Only “sleeves” and “gender” show up, not
“shoe_category” or “brand”. As mentioned, this is
because there are more shirts than shoes.

The number after each facet indicates the number of
records that have that value. We discuss this a bit
more in the section on adding number of facets.
The front-end display: dynamically
displaying the list of facets
The job here is to render the results and facets on the
screen. In terms of UX design, industry standard is to have
the results in the middle, and facets on the left.
There is one container (“facet-lists”) for the 5 facets. The
rendering code generate an unordered list to display the fa
cets in that container.
The results go in the “results-container”.
Next, render the data. As this can take many forms, and
this article is not strictly a tutorial, take a look at
our dynamic faceting GitHub repo for a complete front-end
implementation.
Making the solution more robust
Adding number of facets
You’ll want to let your users know how many records have
a given facet value. Getting the number of facets is useful
because they inform users about the search results. For
example, it is useful to know that there are more shortsleeved shirts than long-sleeved shirts. These numbers of
facets are normally calculated in the back end during query
execution.
Adding facet metadata
Every record needs to contain information that helps the
process know which attributes are facets. To do this, you
need to add facet meta data to each record by using an
additional attribute that defines the record’s facets:
Going one step further, it’s also useful to include the type of
attribute:
With this information, the front-end code can apply a range
slider for price and a dropdown for the sleeves.
Grouping items using facets:
You’ll want to treat color differently from the other
facet attributes. This is because different colors appear
on the same shirt. For this, the logic question is: Do
you need 1 record per shirt that includes an attribute
with all available colors? or 1 record per color, which
requires multiple records for each shirt? Typical
database thinking would say, of course, only 1 record.
However, as discussed at length in our first
article, faceted search is different.
We put all searchable items in separate records. This
allows people to find “red shirts” using the search bar
without needing to click on a color facet.
Our main goal in this article was to add process to data in
our series on facets & data. We described a particular way
to execute a search and display a set of facets, following a
query cycle of request, execution, response, and
display. To stretch your understanding of facets, we did this
within the context of an advanced use case: we made our
facet search dynamic. Dynamic faceting creates a more
intuitive and useful facet search experience, particularly for
businesses that offer a diverse collection of products and
services.
Command languages
Typically think of OS command languages: DOS or Unix.
Interaction paradigm: user enters command, computer carries it out (with
feedback if required) then requests next command.
Cognitive requirements: relate to written languages
Not much new activity in command language development.
Command Language Design
Important design considerations:
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Provide only sufficient functionality for users to carry out tasks
Macro or scripting capability
Consistency in command language design
Command abbreviations
Help feature
actions become commands
objects become arguments
Consistency is very important in command language design
(very taxing on memory, so minimize the number of chunks)
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use of metaphor yields consistent terminology (command names)
(easier to learn, retain, recall -- e.g. up/down not forward/down)
consistent argument ordering
(e.g. first argument is source and second is destination)
consistent naming of command options
(e.g. v for verbose output regardless of command)
Command abbreviation useful for expert user
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important because expert benefits the most from CUI
novice will require/prefer full names
easier to learn if only one abbreviation strategy used.
Multimedia Document Search and Specialized Search:
Multimedia search enables information search using queries in multiple data
types including text and other multimedia formats. Multimedia search can be
implemented through multimodal search interfaces, i.e., interfaces that allow
to submit search queries not only as textual requests, but also through other
media. We can distinguish two methodologies in multimedia search:

Metadata search: the search is made on the layers of metadata.

Query by example: The interaction consists in submitting a piece of
information (e.g., a video, an image, or a piece of audio) for the purpose
of finding similar multimedia items.
Metadata search
Search is made using the layers in metadata which contain information of
the content of a multimedia file. Metadata search is easier, faster and
effective because instead of working with complex material, such as an audio,
a video or an image, it searches using text.
There are three processes which should be done in this method:

Summarization of media content (feature extraction). The result of
feature extraction is a description.

Filtering of media descriptions (for example, elimination
of Redundancy)

Categorization of media descriptions into classes.
Query by example[edit]
In query by example, the element used to search is a multimedia content
(image, audio, video). In other words, the query is a media. Often, it's
used audiovisual indexing. It will be necessary to choose the criteria we are
going to use for creating metadata. The process of search can be divided in
three parts:

Generate descriptors for the media which we are going to use as query
and the descriptors for the media in our database.

Compare descriptors of the query and our database’s media.

List the media sorted by maximum coincidence.
Multimedia search engine
There are two big search families, in function of the content:

Visual search engine

Audio search engine
Visual search engine
Inside this family we can distinguish two topics: image search and video
search
Image search: Although usually it's used simple metadata search,

increasingly is being used indexing methods for making the results of users
queries more accurate using query by example. For example, QR codes.
Video search: Videos can be searched for simple metadata or by

complex metadata generated by indexing. The audio contained in the
videos is usually scanned by audio search engines.
Audio search engine
There are different methods of audio searching:
Voice search engine: Allows the user to search using speech instead of

text. It uses algorithms of speech recognition. An example of this
technology is Google Voice Search.
Music search engine: Although most of applications which searches

music works on simple metadata (artist, name of track, album…) . There
are some programs of music recognition, for
example Shazam or SoundHound.
Image Search – Video Search – Audio Search:
Searching
To search for media with the Shutterstock API, you need:

An account at https://www.shutterstock.com

An application at https://www.shutterstock.com/account/developers/apps

One of these types of authentication:
o
Basic authentication: You provide the client ID and client secret with
each API request.
o
OAuth authentication: You use the client ID and client secret to get
an authentication token and use this token in each request. In the
examples on this page, the access token is in
the SHUTTERSTOCK_API_TOKEN environment variable. For more
information about authentication, see Authentication.
Some API subscriptions return a limited set of results. See Subscriptions in the API
reference.
Keyword searches
To search for media with a keyword, pass the search keywords to the appropriate
endpoint:

Images: GET https://api.shutterstock.com/v2/images/search

Video: GET https://api.shutterstock.com/v2/videos/search

Audio: GET https://api.shutterstock.com/v2/audio/search
The search keywords must be URL encoded and in the query query parameter. The
API searches for the keywords in all textual fields, including but not limited to the
description, keywords, and title. For examples of search results, see Results.
Searches do not support wildcards such as *.
Here are some examples of simple image, video, and audio search requests:
Simple image keyword search
cURL
CLI
PHP
JavaScript
curl -X GET "https://api.shutterstock.com/v2/images/search" \
-H "Authorization: Bearer $SHUTTERSTOCK_API_TOKEN" \
-G \
--data-urlencode "query=kites"
Simple video keyword search
cURL
CLI
PHP
JavaScript
curl -X GET "https://api.shutterstock.com/v2/videos/search" \
-H "Authorization: Bearer $SHUTTERSTOCK_API_TOKEN" \
-G \
--data-urlencode "query=hot air balloon"
Simple audio keyword search
cURL
CLI
PHP
JavaScript
curl -X GET "https://api.shutterstock.com/v2/audio/search" \
-H "Authorization: Bearer $SHUTTERSTOCK_API_TOKEN" \
-G \
--data-urlencode "query=bluegrass"
Conditional searches
Searches for images and video can use AND, OR, and NOT conditions, but searches
for audio do not support these keywords.
To use AND, OR, or NOT in searches, you include these operators in
the query query parameter. The operators must be in upper case and they must be in
English, regardless of the language the keywords are in.

AND is added implicitly between each search keyword. Therefore, searching
for dog AND cat is equivalent to searching for dog cat.

OR searches for results that include any of the specified keywords, such
as dog OR cat OR mouse.

NOT searches exclude keywords from search results, such as dog NOT hot
dog. You can also use NOT in the contributor search field.
You can group conditional search terms with parentheses. For example, to search for
images with either dogs or cats, but not both, use (dog NOT cat) OR (cat NOT dog).
Here are some examples of searching with conditions:
cURL
CLI
PHP
JavaScript
curl -X GET "https://api.shutterstock.com/v2/images/search" \
-H "Authorization: Bearer $SHUTTERSTOCK_API_TOKEN" \
-G \
--data-urlencode "query=dog AND cat"
curl -X GET "https://api.shutterstock.com/v2/videos/search" \
-H "Authorization: Bearer $SHUTTERSTOCK_API_TOKEN" \
-G \
--data-urlencode "query=mountain NOT camping"
Bulk searches
You can run up to 5 image searches in a single request with the POST
/v2/images/bulk_search endpoint. The API returns a maximum of 20 results for each
search. To use this endpoint, pass multiple searches in the queries array in the
request body. Each search in the queries array has the same parameters as an
individual image search.
You can also pass query parameters in the request. These parameters become
defaults for each search, but the parameters in the individual searches override them.
Here is an example of sending 2 searches in a single request:
DATA='[
{
"query": "cat",
"license": ["editorial"],
"sort": "popular"
},
{
"query": "dog",
"orientation": "horizontal"
}
]'
curl -X POST "https://api.shutterstock.com/v2/images/bulk_search" \
-H "Authorization: Bearer $SHUTTERSTOCK_API_TOKEN" \
-H "Content-Type: application/json" \
-d "$DATA"
GEOGRAPHIC INFORMATION
SYSTEMS (GIS) SEARCH:
What We Do
Geographic Information Systems (GIS) is used to enhance decision making and provide
maps and information to the public and County agencies including:

Assessors Office

Community Development Resource Agency

County Executive Office

Elections

Facility Services

Health and Human Services

Public Works
Requests

For zoning information, please email our Public Counter Technicians.

For GIS/mapping requests (not zoning), please email the GIS Division.
Services & Information

Placer County Open GIS Data: Web mapping applications and GIS data download.
The place to find GIS data for Placer County that you can download or incorporate into
existing ArcGIS projects. Here you can also find web apps that include active building permits,
code compliance cases, and planning projects, among others.

Interactive GIS/Parcel Data Search: Search by Assessors Parcel Number or
address for zoning, parcel related information, aerial photography, etc.

Assessment Inquiry Website: Data information from the County Assessment Roll
(assessed values, assessment maps, etc.)

Current Permit Information: Check permit status, permit activity, project descriptions
and other permit-related information

Permit Research Tools: Various tools available for conducting land-use-related
permit research.
Multilingual Searches :
Multilingual meta-search engines: These search engines aggregate results from
multiple search engines, each specialized in a particular language or region.
They provide users with a unified interface to search across different search
engines and retrieve multilingual results from various sources.
A Moroccan immigrant who speaks some French and German in addition to his
Arabic dialect is multilingual, as is the conference interpreter who confidently
uses her three native or first languages of English, German and French as
working languages..
The Main Benefits of Multilingualism



Cognitive Benefits. Learning and knowing several languages sharpens
the mind and improves memory. ...
Improvement of Ability to Multitask. Being able to speak in multiple
languages allows you to perform better in many ways. ...
Improvement of Communication Skills.
DATA VISUALIZATION:
Data visualization is the graphical representation of information and data. By
using visual elements like charts, graphs, and maps, data visualization tools
provide an accessible way to see and understand trends, outliers, and patterns
in data. Additionally, it provides an excellent way for employees or business
owners to present data to non-technical audiences without confusion.
In the world of Big Data, data visualization tools and technologies are
essential to analyze massive amounts of information and make data-driven
decisions.
What are the advantages and disadvantages of data
visualization?
Something as simple as presenting data in graphic format may seem to have
no downsides. But sometimes data can be misrepresented or misinterpreted
when placed in the wrong style of data visualization. When choosing to create
a data visualization, it’s best to keep both the advantages and disadvantages
in mind.
Advantages
Our eyes are drawn to colors and patterns. We can quickly identify red from
blue, and squares from circles. Our culture is visual, including everything from
art and advertisements to TV and movies. Data visualization is another form of
visual art that grabs our interest and keeps our eyes on the message. When
we see a chart, we quickly see trends and outliers. If we can see something, we
internalize it quickly. It’s storytelling with a purpose. If you’ve ever stared at a
massive spreadsheet of data and couldn’t see a trend, you know how much
more effective a visualization can be.
Some other advantages of data visualization include:

Easily sharing information.

Interactively explore opportunities.

Visualize patterns and relationships.
Disadvantages
While there are many advantages, some of the disadvantages may seem less
obvious. For example, when viewing a visualization with many different
datapoints, it’s easy to make an inaccurate assumption. Or sometimes the
visualization is just designed wrong so that it’s biased or confusing.
Some other disadvantages include:

Biased or inaccurate information.

Correlation doesn’t always mean causation.

Core messages can get lost in translation.
Why data visualization is important
The importance of data visualization is simple: it helps people see, interact
with, and better understand data. Whether simple or complex, the right
visualization can bring everyone on the same page, regardless of their level of
expertise.
It’s hard to think of a professional industry that doesn’t benefit from making
data more understandable. Every STEM field benefits from understanding
data—and so do fields in government, finance, marketing, history, consumer
goods, service industries, education, sports, and so on.
While we’ll always wax poetically about data visualization (you’re on the
Tableau website, after all) there are practical, real-life applications that are
undeniable. And, since visualization is so prolific, it’s also one of the most
useful professional skills to develop. The better you can convey your points
visually, whether in a dashboard or a slide deck, the better you can leverage
that information. The concept of the citizen data scientist is on the rise. Skill
sets are changing to accommodate a data-driven world. It is increasingly
valuable for professionals to be able to use data to make decisions and use
visuals to tell stories of when data informs the who, what, when, where, and
how.
While traditional education typically draws a distinct line between creative
storytelling and technical analysis, the modern professional world also values
those who can cross between the two: data visualization sits right in the
middle of analysis and visual storytelling.
TASKS IN DATA VISUALIZATION:
Common roles for data visualization include:






showing change over time.
showing a part-to-whole composition.
looking at how data is distributed.
comparing values between groups.
observing relationships between variables.
looking at geographical data.
Task-based effectiveness of
basic visualizations
This is a summary of a recent paper on an age-old topic: what visualisation
should I use? No prizes for guessing “it depends!” Is this the paper to finally
settle the age-old debate surrounding pie-charts??
Task-based effectiveness of basic visualizations Saket et al., IEEE
Transactions on Visualization and Computer Graphics 2019
So far this week we’ve seen how to create all sorts of fantastic
interactive visualisations, and taken a look at what data analysts
actually do when they do ‘exploratory data analysis.’ To round off the
week today’s choice is a recent paper on an age-old topic: what
visualisation should I use?
…the effectiveness of a visualization depends on
several factors including task at the hand, and
data attributes and datasets visualized.
Saket et al. look at five of the most basic visualisations —bar charts,
line charts, pie charts, scatterplots, and tables— and study their
effectiveness when presenting modest amounts of data (less than 50
visual marks) across 10 different tasks. The task taxonomy comes
from the work of Amar et al., describing a set of ten low-level analysis
tasks that describe users’ activities while using visualization tools.
1.
Finding anomalies
2.
Finding clusters (counting the number of groups with similar
data attribute values)
3.
Finding correlations (determining whether or not there is a
correlation between two data attributes)
4.
Computing derived values, for example, computing an
aggregate value
5.
Characterising distributions, for example, figuring out which
percentage of data points have a value over a certain threshold
6.
Finding extremes (i.e., min and max)
7.
Filtering (finding data points that satisfy a condition)
8.
Ordering (ranking data points according to some metric)
9.
Determining a range (finding the span of values – pretty
straightforward if you can find the extremes – #6)
10.
Retrieving a value (identifying the values of attributes for
given points).
DATA & VIEW SPECIFICATION:
These controls enable users to selectively visualize the data,
to filter out unrelated information to focus on relevant items,
and to sort information to expose patterns.
Data and View Specification involve determining which data is to be shown and visualized with
programs such as Microsoft Excel. Then it involves the filtering of data which shifts the focus
among the different data subsets to isolate specific categories of values. Sorting the data can show
surface trends and clusters and organize data according to a unit of analysis. The following image
shows a more complex form of a matrix-based visualization of a social network.
The first matrix plot shows
a social network when people are sorted alphabetically. The second plot shows a reordering by
node degrees resulting in more structure and the third plot is permutated by network connectivity,
showing underlying clusters of communities. The final step is to derive new attributes from
existing values when input data is insufficient.
View Manipulation :
The second dynamic is View Manipulation which consists of highlighting patterns, investigation of
hypotheses and revealing additional details. Selection allows for pointing to an item of interest, for
example, dragging along axes to "create interactive selections that highlight automobiles with low
weight and high mileage." Navigating is determined by where the analyst begins, such as in a crime
map that depicts crime activity by time and region. Coordinating allows the analyst to see multiple
coordinated views at once which can facilitate comparison. This can be done in histograms, maps
or network diagrams. The following image shows a complex patchwork of interlinked tables, plots
and maps to analyze outcomes of elections in Michigan.
The image shows a combination of tables, plots and maps. The final step, organization, involves
arranging visualization views, legends and controls for more simplified viewing.
Process and Provenance:
The final dynamic is Process and Provenance which involves the actual interpretation of data.
Recording involves chronicling and visualizing analysts' interaction histories in both a
chronological and sequential fashion. Annotation includes recording, organizing and
communicating insights gained during analysis. Sharing involves the accumulation of multiple
analyses and interpretations derived from several people and the dissemination of results. Guiding
is the final step and includes developing new strategies to guide newcomers.
Visualization by Data Type :
Visualizing data is crucial to ensure that all the data you collect
translates into decisions that amplify your business growth. Here
are five data visualizations that are commonly used by companies
across the world.
Common Types of Data Visualizations
1.
Bar Chart
2.
Doughnut Chart or Pie Chart
3.
Line Graph or Line Chart
4.
Pivot Table
5.
Scatter Plot
1. Bar Chart
Bar charts or column charts have rectangle bars arranged on the X
or Y-axis. Comparing objects by aligning them with the same
parameters is the most popular visualization out there. Bar charts
can be used to track changes over time. However, bar graphs used
for time series yield accurate results when the changes are
considerably large. There are different categories of bar graphs like
stacked bar graphs, 100% stacked bar graphs, grouped bar graphs,
box plots, and waterfall charts (advanced bar graphs).
Bar charts are most suitable for:

Comparing a numerical value across categories

Identifying the order within a category

Representing a histogram (where the values on the X-axis are
grouped into buckets)
Use Case: You can use a bar chart for a visual representation of
your overall business revenue against your peers. If you want to
compare the individual split of product-wise revenue, your best
choice would be a stacked/grouped bar chart.
2. Doughnut Chart or Pie Chart
A Doughnut chart slices a doughnut into multiple parts based on
the field value. Doughnut charts or pie charts are suitable to depict
parts of a whole relationship, where all units together represent
100%. Drilled-down doughnut charts are interactive and help users
decipher complex data to get to the source of the issue or the
solution.
Use Case: You can analyze your budget using a doughnut chart.
Splitting the total budget across your expenses, investments, loan,
savings, etc. would give you an instant understanding of your
budget plan.
3. Line Graph or Line Chart
Line graphs have values plotted as lines across the X and Y-axis.
They are used to track changes over a short/long time frame. Line
graphs are better to use than bar graphs particularly when the
changes are minor. You also have the multi-line graph option when
you need to compare changes over the same time period for more
than one attribute.
Line graphs are best used to:

Display trend over a time series

Pinpoint outliers

Visualize forecasted data
Use Case: Say, you want to analyze your business’ month-wise
expenditure. The line graph will give you the best rendition by
plotting the values across months on the X-axis and expenditure on
the Y-axis.
4. Pivot Table
Pivot table as the name indicates has columns and rows with
aggregated values populated in the cells. The pivot table is the most
straight-forward visualization that can be used to convey a huge
amount of data at a single glance. It is easy to build and flexible to
modify. However, unlike the other infographic visualizations
discussed here, tables are not graphical and hence can be used only
in specific cases —

When you want to compare different unrelated metrics
required

When there are relatively lesser rows (display of data at the
top level as opposed to the granular level)
Use Case: Financial reports are generally depicted over tables.
Bringing in the years on rows and operating cash flow, investing
cash flow, cash from financing, and other metrics on the columns
will help you understand your business’s cash flow over the years.
5. Scatter Plot
Scatter plot shows the relationship of the common attribute
between two numerical variables plotted along both X and Y axes.
If you are a data scientist working with different sets of data,
scatter plot would be something that you commonly work with,
but for a novice user, it could be a little unfamiliar. Scatter plots
are best suitable to compare two numerical values simultaneously.
Segmentation charts and bubble charts are the advanced versions
of the scatter plot. The segmentation chart demarcates the scatter
plot into four quadrants, making the choice of the users easier.
Bubble sort brings in an additional dimension to the chart by
displaying varied sizes of bubbles over the scatter plot.
Use Case: You can present data for product price revision using the
scatter plot by bringing in the number of units sold on the X-axis,
current price on the Y-axis, and the products on the quadrant. It
will give you a clear outlook on the products that have a low price
yet have sold a good deal and can be considered for price
increment. Alternatively, you can bring in a price drop for
products that have a high price but are in low demand.
These types of charts, along with area charts, heat maps, and
treemaps are widely used visualization techniques by data analysts,
marketers, and financial analysts across the world. However, there
are specific visualizations that can be used to tackle the reporting
needs of unique data sets – for instance, it would be ideal to use
choropleth (Map chart), tree diagram, and radar chart for
geospatial, conditional, and multivariable data points respectively.
Choosing the perfect visualization from different types of data
visualizations can be challenging, but with a basic understanding of
these fundamental charts, your choice will be easier.
Challenges for Data Visualization:
Data visualization is a quick and simple technique to depict complicated
ideas for improved comprehension and intuition graphically. It must find
diverse relationships and patterns concealed by the massive amounts of
data. We can use traditional graphical representations to organize and
represent data. Still, it can be challenging to display Huge amounts of
data that is very diverse uncertain, and semi-structured or unstructured
in real-time. Massive parallelization is required to handle and visualize
such dynamic data. In this article, we will cover the major challenges
faced by big data visualization and its solutions.
Content
Data Quality


Accuracy

Completeness

Consistency

Format

Integrity

Timeliness

Not Choosing the Right Data Visualization Tools

Confusing Color Palate

Analytical & Technical Challenges
Data Quality
The data quality plays a crucial role in determining its effectiveness. Not
all data is created in the same way, and each has a different origin,
hence its heterogeneity.
No matter how powerful and comprehensive the big data tools at the
organization’s disposal are, insufficient or incomplete data can often lead
data scientists to conclusions that may not be entirely correct and,
therefore, could negatively impact business.
The effectiveness of big data in analysis depends on the accuracy,
consistency, relevance, completeness, and updating of the data used. With
these factors, the data analysis ceases to be reliable.
Accuracy
The most important thing is: How accurate is the data? How much can
you trust it? Is there certainty about the collection of relevant data? The
values in each field of the database must be correct and accurately
represent “real world” values.
Example: A registered address must be a real address. Names must be
spelled correctly.
Completeness
The data must contain all the necessary information and be easily
understandable by the user.
Example: If the first and last name are required in a form, but the
middle name is optional, the form can be considered complete even if the
middle name is not entered.
Consistency
The data must be the same throughout the organization and in all
systems.
Example: The data of a sale registered in the CRM of a company must
match the data registered in the accounting dashboard that you manage.
Format
The data must meet certain standards of type, format, size, etc.
Example: All dates must follow the format DD/MM/YY, or whichever
format you prefer. Names should only have letters, no numbers or
symbols.
Integrity
The data must be valid, which means that there are registered
relationships that connect all the data. Keep in mind that unlinked
records could introduce duplicate entries to your system.
Example: If you have an address registered in your database, but it is not
linked to any individual, business, or other entity, it is invalid.
Timeliness
Data must be available when the user expects and needs it.
Example: A hospital must track the time of patient care events in the ICU
or emergency room to assist doctors and nurses.
Not Choosing the Right Data Visualization Tools
The selection of big data tools often focuses on the technical plane, leaving
aside everything that is not directly related to the analysis. Acting this
way means ending up implementing solutions whose visualization
potential is narrower.
When this happens, the consequences do not take long to appear:

Causing difficulties in understanding the data.

Subtracting agility from the process of extracting and sharing
knowledge within the organization.

Increasing latencies in taking action.

Being able even to divert decision-making, which would lose
effectiveness.
Many data visualization tools, such as Tableau, Microsoft Power BI,
Looker, Sisense, Qlik, etc., offer data visualization integration capabilities.
If your organization already uses one of these tools, start there. If not, try
one. Once you select a tool, you’ll need to do a series of prototypes to
validate capabilities, ease of use, and operational considerations.
Here is a detailed list of considerations:

Do the chart types meet the business needs

How easy is it to integrate?

What are the flexibilities in device design and compatibility?

Is the security configurable for the required end user/consumer
rights?

Is it performing fast enough to integrate into an application?

Are the platform’s costs and pricing models aligned?