Fundamentals of GIS

GIS Applications in Civil Engineering

Carolyn J. Merry

Dept. of Civil & Environmental Engineering & Geodetic Science

College of Engineering

merry.1@osu.edu

Fundamentals of GIS

Civil Engineering Applications

• Transportation• Watershed analysis• Remote sensing

Fundamentals of GIS

Location-

Allocation

• Finding a subset of locations from a set of potential or candidate locations that best serve some existing demand so as minimize some cost

• Locate sites to best serve allocated demand

• Application areas are warehouse location, fast food locations, fire stations, schools

Fundamentals of GIS

Location-

Allocation Inputs

• Customer or demand locations• Potential site locations and/or

existing facilities• Street network or Euclidean

distance• The problem to solve

Fundamentals of GIS

Location-

Allocation Outputs

• The best sites• The optimal allocation of demand

locations to those sites• Lots of statistical and summary

information about that particular allocation

Fundamentals of GIS

Initial Configuration

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Availabl

e Site

s

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Final

Configuration

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Vehicle Routing

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Synergy between spatial data and analysis

• Imagine you are a national retailer

• You need warehouses to supply your outlets

• You do not wish the warehouses to be more than 1000 km from any outlet

(Example from Jay Sandhu, ESRI)

Fundamentals of GIS

Demand (population density

)

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Possibl

e Candida

te Sites…?

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Feasibl

e Candida

te Site

s

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Optimal One Site

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Optimal Two Site

s

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Optimal Six Site

s

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Optimal Nine

Sites

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Coverage vs. Distance

(From Jay Sandhu, ESRI)

Fundamentals of GIS

Other Transportation Applications

• Planning & locating new roadway corridors

(from NCRST-E)

Fundamentals of GIS

Transportation – Emergency Operations

• Transportation maps are critical• Disaster response plans can be

developed• Outside computer models used for

advance warnings• Land use maps enhance emergency

operations

Fundamentals of GIS

Mean Household Evacuation

Time

Standard Deviation in Household Evacuation

Time

Mean Household Evacuation

Time

Standard Deviation in Household Evacuation

Time

(1 exit route) (2 exit routes)

(from NCRST-H)

Evacuation scenario

Fundamentals of GIS

Watershed Characterization

• Relate physical characteristics to water quality & quantity

• Data – land use & land cover, geology, soils, hydrography & topography – related to hydrological properties

Fundamentals of GIS

Watershed Applications

• Estimate the magnitude of high-flow events, the probability of low-flow events

• Determine flood zones• Identify high-potential erosion areas• For example, BASINS, HEC-RAS,

MIKE11 models integrated with GIS

• cross sections

• assumed cross sections

• boundary conditions

Cross sections

• gaging station

• water treatment plant

• wastewater treatment plant

Boundary conditions

0

100

200

300

400

500

600

700

11/1/1998 2/9/1999 5/20/1999 8/28/1999 12/6/1999 3/15/2000Time (date)

Flo

w (

m3/

sec)

measuredcalculated

03231500

Fundamentals of GIS

Slope Stability Analysis

• Derive physical characteristics– area, perimeter, flow path length, maximum

width, average closing angle, watershed topology, soil data

• Derive watershed characteristics– watershed boundaries, drainage network,

slope & aspect maps

Watersheds Land use

Soils types

DEM with drainage network

Hydrologic modelsHydrologic models

USGS empirical method

TR55

Area- Discharge method

ADAPT model

Portage River Basin, Ohio

ADAPT's Hydrological Output for Needles Creek at County Line Rd for 2001

0.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

120 140 160 180 200 220 240 260

Days

Tota

l dai

ly ru

noff

( in)

ADAPT

Pressure Transducer

Fundamentals of GIS

Remote Sensing

• Image backdrop• Source of information on:

– land use/land cover– vegetation type, distribution, condition– surface waters– river networks– geomorphology– monitor change

1984 Land Use Map

Land use

Water: 249.43 km2

Urban: 1348.53 Km2

Forest: 10700.92 km2

Agriculture: 17780.62 km2

Pasture: 175.50 km2

Grass: 2609.45 km2

1999 Land Use Map

Land use

Water: 268.74 km2

Urban: 2312.35 Km2

Forest: 11182.39 km2

Agriculture: 16675.65 km2

Pasture: 1308.23km2

Grass: 1518.18 km2

Urban Area Change from 1984 - 1999

Urban Area, 1984Urban Area, 1999

Landuse 1984(km2) 1999(km2) Change % Ashland Urban 2 5 5 2 3 5 .7 Ashland Agriculture 5 0 4 4 7 9 -2 .6 Crawford Urban 2 6 4 3 2 4 .9 Crawford Agriculture 7 2 3 8 0 4 5 .3 Delaware Urban 4 2 9 8 4 0 .5 Delaware Agriculture 7 0 7 6 5 7 -3 .6 Fairfield Urban 3 6 9 4 4 4 .5 Fairfield Agriculture 7 3 7 6 6 0 -5 .5 Franklin Urban 4 1 1 6 8 5 2 5 .0 Franklin Agriculture 6 1 3 4 1 0 -1 9 .8 Holmes Urban 1 7 4 7 4 6 .4 Holmes Agriculture 4 0 3 3 8 5 -2 .3 Knox Urban 1 7 3 7 3 7 .1 Knox Agriculture 6 5 8 6 2 6 -2 .5 Licking Urban 5 4 1 0 2 3 1 .2 Licking Agriculture 8 5 8 7 2 5 -8 .4 M adison Urban 2 2 3 7 2 5 .0 M adison Agriculture 8 9 8 1 0 1 7 6 .2 M arion Urban 4 4 6 4 1 8 .3 M arion Agriculture 7 4 3 8 1 9 4 .9 M orrow Urban 1 2 2 2 3 1 .2 M orrow Agriculture 6 1 5 6 6 2 3 .7 Perry Urban 1 4 2 6 3 2 .0 Perry Agriculture 3 6 6 2 2 4 -2 4 .0 Richland Urban 4 7 7 3 2 1 .5 Richland Agriculture 5 8 7 5 9 4 0 .6 Union Urban 3 0 4 2 1 7 .1 Union Agriculture 7 9 2 8 4 9 3 .5 Wayne Urban 7 7 1 0 6 1 5 .8 Wayne Agriculture 7 1 5 7 5 1 2 .4 Wyandot Urban 2 7 6 9 4 4 .7 Wyandot Agriculture 7 8 4 7 8 7 0 .2

MSS data - 19 Jun 75 MSS data - 1 Aug 86 TM data - 22 Jun 92

Fundamentals of GIS

Stream Water Quality in the Maumee River Basin

9 Landsat-7 images over the Waterville station in the Maumee River Basin were selected.

A 3-by-3 pixel window over the Waterville station for each date was converted to % reflectance values. A least squares regression was used to correlate these % reflectance values with USGS ground data on suspended sediment concentration collected at the Waterville station.

Maumee River Basin

Ln(Y) = -0.125 + 1.39Ln(B2) + 1.03Ln(B3/B4) 84.1

(%) Proposed Equation r

Y = Predicted Suspended Sediment Concentration (mg/L)

B1,B2,B3,B4 = Reflectance (%) in ETM+ Bands 1,2,3,410

100

4 6 8 10 12 14 16

Reflectance (%)

Susp

ende

d Se

dim

ent C

once

ntra

tion

(mg/

L)

Date Suspended Sediment Average Concentration (mg/L) Reflectance (%)

15-Jul-99 27 11.616-Aug-99 22 9.11-Sep-99 19 8.217-Sep-99 14 7.84-Nov-99 8 4.5

27-Mar-00 56 9.514-May-00 45 12.9

1-Jul-00 62 9.819-Sep-00 81 14.8

Suspended Sediment Concentration Model Waterville Station – Maumee River Basin, Ohio

Scale (Km)

20 0

27 March 2000 (56)

W

14 May 2000 (62)

1 July 2000 (45)

W

19 September 2000 (81)

WW

Fundamentals of GIS

Example Applications

• Links to websites– The District– Urban development– Lake Superior– Rutgers University– OhioView