Annual Meeting 2016

Understanding Map Integration

Using GIS Software

7/28/16 Michelle Pasco

USRIP Symposium

Introduction to GIS �  Geographic Information

System (GIS)

�  Used to study all kinds of data with a geospatial component

�  Digitizes maps using vector components (points, lines, polygons)

�  Representation of the real world and its attributes

Credit: desktop.arcgis.com

Map Integration �  Also known as “conflation”

�  Combination of two or more datasets to provide new

perspectives and insight on existent geo-enabled data sets

�  May result in a number of problems

�  This project attempted to research and conflate two data sets within GIS �  Virginia Department of Transportation’s (VDOT) Linear

Referencing System (LRS) �  INRIX XD (XD)

Study Area

Interstates I-64, I-564, I-95, I-395, and I-495 highlighting the study areas.

Issues faced �  Spatial displacement and attribute disparity

�  Length, position, direction, size, shape �  Feature representation

�  Unequal updating periods, equal data models acquired

by different operators, unequal data models, and content differences

Methods of Conflation �  Spatial Join – combines two datasets by comparing their

digitized geometries and creating a count recording either features in close proximity or complete matches

Spatial join conflation on part of I-64.

Methods of Conflation �  Transfer Attributes – matches a feature from one dataset

to another feature by selecting one attribute that is similar in both datasets within a certain distance

Transfer attributes conflation on part of I-64.

Comparison Cases

Spatial Join �  Matching Geographic

Coordinate Systems (GCS) vs Original = possibly different GCS (ORG)

�  LRS EDGE (EDGE) vs LRS Non-EDGE (NON)

Transfer Attributes �  Search Distances

�  0.1 miles �  0.3 miles �  0.5 miles �  1 mile

Accuracy Assessment �  Spatial Join:

�  Transfer Attributes:

Results: Spatial Join

Visual representation of spatial join cases on part of I-64.

EDGE_ORG

EDGE_GCS

NON_ORG

NON_GCS

LRS EDGE = EDGE LRS Non-EDGE = NON

XD Original = ORG XD Geographic Coordinate System = GCS

Results: Spatial Join Road Name &

Spatial Join # of features (count>0) features

Conflation Accuracy, ca (%)

I-64 EDGE_ORG 728 632 86.81

I-564 EDGE_ORG 12 10 83.33

I-95 EDGE_ORG 573 452 78.88

I-395 EDGE_ORG 136 89 65.44

I-495 EDGE_ORG 167 102 61.08

Road Name & Spatial Join # of features (count>0)

features Conflation

Accuracy, ca (%)

I-64 EDGE_GCS 728 359 49.31

I-564 EDGE_GCS 12 10 83.33

I-95 EDGE_GCS 573 287 50.09

I-395 EDGE_GCS 136 27 19.85

I-495 EDGE_GCS 167 30 17.96

Results: Transfer Attributes

Visual representation of transfer attribute cases on part of I-64.

0.1 mile Search Distance

0.3 mile Search Distance

0.5 mile Search Distance

1 mile Search Distance

Road Name & Search Distance # of features No <Null>

features Conflation

Accuracy, ca (%)

I-64_0.1 mi 754 686 90.98

I-564_0.1 mi 11 8 72.73

I-95_0.1 mi 477 435 97.32

I-395_0.1 mi 64 60 93.75

I-495_0.1 mi 52 50 96.15

Road Name & Search Distance # of features No <Null>

features Conflation

Accuracy, ca (%)

I-64_0.3 mi 754 689 91.38

I-564_0.3 mi 11 8 72.73

I-95_0.3 mi 477 435 97.32

I-395_0.3 mi 64 61 95.31

I-495_0.3 mi 52 50 96.15

Results: Transfer Attributes

Results: Buffer Tool

0.1 0.3 0.5 Flat

Seg 1 1 1 1 1

Seg 2 3 3 3 2

Seg 3 2 2 2 2

Seg 4 2 2 2 1

0.1 0.3 0.5 Flat

4100330 2 2 2 2

4100331 3 3 3 2

4100515 3 3 3 2

Visual representation of the types of buffers on part of I-64.

LRS Matching XD Matching

Conclusions �  Transfer attributes is overall more accurate

�  Covers the two most important aspects in the conflation process: spatial data and attributes

�  Spatial joining is better to use if the datasets are comprised of many, potentially small, features

�  Either way, larger-scale projects will be more vulnerable to issues

Questions?

Acknowledgements

� Simona Babiceanu, who advised me and kept me on the right track

� Dr. Emily Parkany, for the constant support � Daniela Gonzales, for encouraging me to

apply to this program

References �  Davis, Curt H., Haithcoat, Timothy L., Keller, James M., Song, Wenbo. Relaxation-

Based Point Feature Matching for Vector Map Conflation, 2011. Transactions in GIS, 15(1), pg. 43-60. http://onlinelibrary.wiley.com/doi/ 10.1111/j.1467-9671.2010.01243.x/full. Accessed June 20, 2016.

�  Environmental Systems Research Institute (ESRI). ArcGIS for Desktop, 2016. arcgis.com. Accessed July 18, 2016.

�  G. v. Gösseln, M. Sester. Integration of Geoscientific Data Sets and the German Digital Map Using A Matching Approach. Commission IV, WG IV/7. http:// www.cartesia.org/geodoc/isprs2004/comm4/papers/534.pdf. Accessed June 15, 2016.

�  INRIX. I-95 Vehicle Protection Project II Interface Guide, 2014. http:// i95coalition.org/projects/vehicle-probe-project/. Accessed June 17, 2016.

�  Virginia Department of Transportation. Roadway Network System. Release Notes, Linear Referencing System, Version 15.2, 2015. https://www.arcgis.com/ home/item.html?id=60916ea827544412ad209ea5192ad7fd. Accessed June 2, 2016.