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Three Times a Week: Mapping the Transportation of Dialysis Patients Dayton, Ohio Ambreen Hasan Research Analyst Lakeland Community College 2013 Ohio GIS.

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Presentation on theme: "Three Times a Week: Mapping the Transportation of Dialysis Patients Dayton, Ohio Ambreen Hasan Research Analyst Lakeland Community College 2013 Ohio GIS."— Presentation transcript:

1 Three Times a Week: Mapping the Transportation of Dialysis Patients Dayton, Ohio Ambreen Hasan Research Analyst Lakeland Community College 2013 Ohio GIS Conference September , 2013 | Columbus Marriott Northwest | Dublin, Ohio Langdon Sanders GIS Technician City of Kettering, OH

2 Three Times a Week: Mapping the Transportation of Dialysis Patients in the Greater Dayton Area Ambreen Hasan and Langdon Sanders Sponsored by Ohio GIS Conference, Sept. 13, 2013

3 Examine current transportation system for dialysis patients in Montgomery, Miami & Greene co. Inform Transit, Medical, and Public communities – Identify Target Areas, Issues & Challenges Towards Improving Service, Reducing Cost Purpose

4 Research Questions Where are the patients? – “Hot Spots” and rideshare possibilities How do they travel to dialysis? – Field Observation, Patient Survey Are they going to the closest center? Raw Data to Master Table Pickup/Dropoff, Provider, Trip_ID... Geocode Origin & Destination Addresses Mapping Analysis via unique TRIP IDs

5 Direct Observation Findings

6 Selected Survey Results

7 Geocoded points by transit provider Where are the Patients?

8 Created a density surface using Spatial Analyst Notes: We used kernel density Pick a search radius – play with results Cell size det. ‘smoothness’ Lowest color empty Where are the ‘hotspots’?

9 Areas with high percentages of households without a vehicle Accessibility to Personal Transport

10 Density of Patients & Public Bus Routes Accessibility to Public Transport

11 Geocoded PickUp & DropOff Addresses, dialysis centers XY to Line tool – Org.  Dest. – Kept TRIP_ID Distance Traveled Nearest Center Ratio of distance – Actual / Closest Results – 2/ 2 = – Or 2.5/ 2 = 1.25 Travel Efficiency: Closest Dialysis?

12 40% not going to nearest center – Weighted by GDRTA with 111 trips Nearest Center Results

13 RideShare Analysis: Day & Provider

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17 Where do we go from here? Patients Educate & empower of options available (such as where other centers are located & rideshare opportunities) Transit Providers Further study Show mismatch of service duplication and inefficiencies Avoid trip duplication Promote rideshare Talk with other providers Medical Community Work with patients to ID willingness to change Explain dialysis center assignment process - include transportation in decision ID where changes can/cannot be made Implications for Decision Makers

18 Only part of the system – Data from RTA, Anton’s, Greene CATS, MCPT and Fairborn Sen. Center. Straight lines distance used instead of actual distance. – Easy, does not require network Different date ranges of data. Surveyed & Observed only two centers – both in Montgomery County Limitations & Thoughts for Future

19 Ambreen Hasan Research Analyst Lakeland Community College Langdon Sanders GIS Technician City of Kettering (937) Thank you.


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