nr 422- project management jim graham spring 2010
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NR 422- Project Management
Jim Graham
Spring 2010
Credit
• Always give credit to your sources– Name, organization, URL, etc.– To not do so:
• In school is cheating• Out side school; it’s against the law
• You are required to do your down work– Data can be created as a team– Each final product must be assembled
individually
Dave’s Book – GIS Review
• Chapter 1: All
• Chapter 2: 2.1, 2.2.1, 2.5
• Chapter 3: All
• Chapter 4 to 4.2.2– Also: 4.2.6, 4.6
Standard Map Elements
• Title
• Maps
• Legend
• North Arrows
• Scale Bars
• Sources, author(s)
• Projection, Datum
• Regional Map
GIS Professional
• Produce GIS reports, articles, posters, and web sites that are:– Accurate & Precise– Astatically pleasing– Informative– Legal– Include the standard cartography elements:
• Spatial data, legends, scale bars, north arrows, regional maps, credits, author(s), and projection & datum
• Are recognized as authors!
GIS Data
• Attributes (dbf)
• Spatial Data (shp)
• Metadata (xml)
• Projection (prj)
• Joins and Relationships
• Processing Models
• Layer files: Symbology
Various Formats
• Shapefile
• Coverage
• Rasters
• GeoDatabases
• All can be thought of as:– Spatial data – Attribute table
Definitions• Shape
– Points, polylines, or polygons describing one feature on the earth
• Feature– A Shape with attributes
• Feature class – one type of feature (point, polygon, polyline)
• Collection of features– Group of features of the same type (Shapefile)
• Dataset– Set of related collections of features (i.e. the
Shapefiles for Rocky)
Arc Data Storage
Shapefiles Coverages GeoDatabases
Collection of datasets
Folder of Shapefiles
ArcInfo Workspace
GeoDatabase
Dataset Folder of Shapefiles
Coverage Feature dataset
Collection of features
Shapefile Feature class Feature class
Features Point, Multipoint, Polygon, Polyline
Point, Polygon, Polyline, etc.
Point, Multipoint, Polygon, Polyline, Network Annotation
GIS Data Flow
Project, resample
ProcessingProcessed
Data
OriginalData
Final data, mapsTables, text
Analysis
Project Organization
• Project– Original Data
• Workspace for Coverages (folder)• Folders based on topic
– Processing Data• Folders based on projection/datum
– Folders based on topic
– Final Data• Folders based on projection/datum
– Folders based on topic
GIS Data Organization
• One project– Original Data– Processing– Final data
• Multiple projects– Each projects data– Reused data
• Original Data• Processing• Final data
The 7 Habits of Highly Effective People
• 1. Be Proactive
• 2. Start with the end in mind
• 3. Put First Things First
• 4. Think Win-Win
• 5. Seek First to Understand, Then to be Understood
• 6. Synergize
• 7. Sharpen the Saw– Steven Covey
Scheduling
• Define the deliverables/products
• Document the deadline
• Work backwards
• Multiply the schedule by 1+?
Schedule
1. Deadline and deliverables/products
2. Reviews and updates
3. Creating documents (inc. web sites)
4. Analysis
5. Processing
6. Data preparation
7. Acquiring data
8. Proposal review
9. Proposal process
Jim’s Habits
• Panic up front – when it will make a difference
• Check your resources
• Have backups
• Don’t stay stuck (20 minutes)
• Do what it takes to deliver
• Find out what customers really want
• Do the right thing
• Model the behavior you want in others
The Tire Swing
What the customerneeded
What wasdesigned
What marketing suggested
What management approved
What was delivered
Alan Chapman, http://www.businessballs.com/treeswing.htm
Documentation
• Maintain Metadata throughout the project– Make notes in “readme.txt” files in each
folder– Go back and fill in the metadata when you
have time
• Critical:– Sources: location and names– Accuracy, Precision, Error Rate
Working with Others
• Listen, really listen
• What is important to them?
• Divide up tasks:– Large enough for each person to make
progress– Fit the task to the person– Coordinate, don’t micro manage– Check on progress: weekly to monthly
Budgets
• Overhead: ~50%– Administration– Physical Space– Networks/Internet– Phones– Office Supplies– Heating & Cooling
• People– Salary– Benefits
• Computers• Data
– Remotely sensed– Field data collection
• Printing
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