Transcript Utilization of Data - Traffic Records Forum

UTILIZATION OF DATA
MARYLAND HIGHWAY SAFET Y
OFFICE/WASHINGTON COLLEGE
Sean Lynn
BACKGROUND
FUTURE FOR US
DATA
 Understand
 Correct
 Share
DEFINITIONS TO KNOW
 Data: The facts and figures collected, analyzed, and
summarized for presentation and interpretation
 Data Set: All the data collected in a particular study.
 Data Mining: The process of using procedures from statistics
and computer sciences to extract useful information.
DATA FROM A FAR
 Could be excel? direct connection? SQL Database?
WHAT TO DO WITH THE DATA
 Data Correction
 Excel and etc.
 Python Scripting
 Web Development
 Frameworks
 PHP
 JS
 CSS
 Bootstrap
 Databases
 Types
 Schemas
DATA CORRECTIONS
 When you understand the data that you have this will give you
the ability of cleaning it up
 Excel PivotTable/Formulas
 Examples of Excel Formulas
 Date Correction
 text(XX,100)
 Scripting can come in handy with
repeat errors
 Examples for Scripting:
 Names that are incorrectly
spelled
 Flagging fields to review
 For ArcGIS
 Using Joins
 Viewing the data visually
WEB DEVELOPMENT
 Frameworks
 Designed to support the development of services and resources
 Examples of Frameworks
 PHP
 CodeIgniter
 Laravel
 JS
 Highcharts/d3.js (software)
 Node JS
 .NET
 CSS
 Bootstrap Framework
 Server or
Web Service Provider (GoDaddy)
WEB DEVELOPMENT CONT.
 Examples of Databases
 SQL Lite




Small in size
Lite in processing
Performs well with less than 100k uses a day
Good for smaller datasets, surveys, and logins
 MYSQL/PostgreSQL or Postgres




Average in size
Average in processing
Performs well with more than 100k uses a day
Good for large datasets, surveys, and logins
 Data Querying
 Excel, Access, ArcMap, and etc.
WEB DEVELOPMENT CONT.
 Schema for a Relational Database – MYSQL WorkBench
WEB DEVELOPMENT CONT.
 D RO P TA B L E I F E X I S T S ` c r a s h ` ;
 C R E AT E TA B L E I F N OT E X I S T S ` c r a s h ` (
 ` i d ` I N T N OT N U L L ,
 ` d a t a p o i n t s _ i d ` I N T N OT N U L L ,
 ` d et a i l o f t yp e _ i d ` I N T N OT N U L L ,
 P R I M A RY K E Y ( ` i d ` ) ,
 I N D E X ` f k _ c r a s h _ d a t a p o i n t s 1 _ i d x` ( ` d a t a p o in t s _ id ` A S C ) ,
 I N D E X ` f k _ c r a s h _ d et a i lo f t yp e 1 _ i d x` ( ` d et ai l o f t y p e _ id `
ASC),
 CONSTRAINT `fk_crash_datapoints1`

FO R E I G N K E Y ( ` d a t a p o in t s _ id ` )

REFERENCES `datapoints` (`id`)

O N D E L E T E N O AC T I O N

O N U P DAT E N O AC T I O N ,
 C O N S T R A I N T ` f k _ c r a s h _ d et a i l of t yp e 1 `

FO R E I G N K E Y ( ` d et a i lo f t y p e _ id ` )

R E F E R E N C E S ` d et a i l o f t yp e ` ( ` i d ` )

O N D E L E T E N O AC T I O N

O N U P DAT E N O AC T I O N )
 ENGINE = InnoDB;
DATA VISUALIZATION
 $results = Crashes::select('id’, ’crash_type’)->get();
 Time, Day, Time of Day, Temporal Topology
 Infographics –Vennage/WC Developed
QUESTIONS?
Contact Information
[email protected]