Interesting article about the art that is mapmaking...
This is the reason I love old maps....
http://www.slate.com/articles/arts/culturebox/2012/01/the_best_american_wall_map_david_imus_the_essential_geography_of_the_united_states_of_america_.2.html
Tuesday, January 3, 2012
Sunday, May 8, 2011
Rural EMS
Emergency Response Mapping
Statement of the Problem
In the Emergency Medical Services community, time is always considered the most important factor in providing quality care and increasing survivability statistics. It has been accepted as general knowledge that increases in time to initial care, access to advanced life support, arrival at appropriate medical facilities and finally to advanced interventions are all key to positive outcomes to emergency situations. This time saving is always the top priority within a community’s Emergency Medical Service’s plans for improvements. When viewing rural area emergency systems, however, this time saving may be compromised because distances from Rescue Squads and Fire Departments to individual residences can be greater than 20 miles. Often, the ambulance being dispatched to a particular incident is not the closest unit to the scene. This increases response times, making the time factors discussed above nearly impossible to meet. This is the problem which needs evaluation and remediation.
Theoretical Framework
Beginning in the early 1980’s, EMS providers began looking at the effects of response times on their patients. With advances in the 1970’s in Advanced Life Services, especially in cardiology and traumatic injury, the difference in outcomes became obvious for those who received the same treatments at different time intervals. One of the first EMS programs to evaluate time as a real variable in outcomes in emergency situations was in Seattle. The article by Jonathan Mayer discusses how data collected across the entire city showed certain areas had longer response times than other areas. It also showed that the supply and demand model of EMS did not necessarily apply. Areas that were assumed to be high volume areas still saw better outcomes because of quicker response times, despite being given a heavier volume. Those areas that were deemed not as busy and therefore given less resources (ambulances and fire trucks) saw worse outcomes even though there was a unit available to the area far more often, simply because the time to respond was greater due to the longer distance. (Mayer, 71) This geographically disaggregated response area was found to be less effective. Even when applying the outliers and variables to accommodate population density, income and health indices, the results were still statistically significant. (Mayer 73) This is the beginning of the theory for the changes proposed below.
Along with the potential outcomes of individuals facing emergencies, local governments are tasked with finding a way to make providing services to the community more efficient and less costly. Ambulances cost well over $100k, and staffing full time employees with the proper training can cost another $200k a year for each ambulance in service. Since more local governments are feeling the economic strain of the current financial crisis, models and algorithms are entering the conversation that find ways to maximize efficiency and minimize cost. Ideas, especially the ones discussed by Paul Sorensen and Richard Church include shift hours and rotations, locations of resources, dispatching models, types of staffed ambulance (Basic vs. Advanced Life Support) and even the backfilling of available units into areas where units are currently on calls. (Sorensen, 145) All these issues play a part in determining how a system should adjust and change structures to meet the demands and individual natures of the community it serves.
Providing emergency services in the rural setting complicates the equation. By expanding the distance a single unit has to travel, both to the call location as well as the hospital and back makes for extended transit times. As was seen in the Mayer article, times do play a critical role. At the same time, those distances equal increased costs in vehicle maintenance and gas consumption. Compound this with a rural community lacking the tax base or population to support the system fully and a community or local government has a potential crisis in waiting. To complicate things even more, most rural settings do not have the appropriate resources to train and hire their own staffs, so contracts have to be made with private companies, which often ends up costing the locality even more money. It becomes obvious that the localities in rural settings should make every effort to establish cost saving practices. This becomes the framework for the proposed dialogue presented below.
Thesis Statement
When measuring response times in rural Emergency Medical Services, combining traditional county dispatch systems into a single system will provide for faster and more efficient response times than the current system, resulting in quicker care and better patient outcomes.
Measurement/Methodology
Factors making the feasibility of combining systems are the first issue that needs evaluation. In this proposed case study, the counties of Southampton, Surry and Sussex Virginia will be combined. These three counties are contiguous, connected by multiple roads. They are also currently employing contractual Emergency Medical Technicians from the same company, Medical Transport, Inc., a division of Sentara Enterprises, as their primary Emergency Medical Service providers. Because of the familiarity and continuity of business practices and Operational Medical Direction, the combination of these three counties EMS dispatch systems is more feasible than a region of the state in which no connections exist between county EMS systems.
To provide analysis of the current system, maps of the three counties will be provided. Overlayed onto these basic maps will be the current 1st due run areas of the 7 Rescue Squads servicing the 3 counties. A 1st due run area is the area in which a call received in the dispatch center would be given to a particular station. A network analysis tool, providing times to individual locations within the county will be presented. The analysis will be to measure the current times to locations throughout the three counties. The proposed maps will use the current network analysis of times to locations to redraw 1st due run areas, based on time and disregarding traditional county lines. The same data can be overlayed to present improvements in potential health outcomes, based on the time to critical care.
Expected Results
The expected results will include the discovery of locations within the current county lines that have prolonged response times, due simply to excessive distance from the scene. The proposed map will provide a comparison to show that by ignoring classic boundaries, such as county and municipal lines, a significant savings in time for responding to emergency situations can be made. This provides for better patient outcomes, and increased efficiency and cost saving for all three counties.
Report
The maps did tend to show the expected results, although in a few places, there were some unexpected results. These will be discussed further below, as well as the methodological process of data collection, integration and analysis.
The data was collected from two main sources. First, the road map, used to produce times of transit, was provided by ESRI US National Road Map Atlas. This map provided every road in the three counties, as well as their speed limits, which was necessary to establish the length of time it took to travel a certain distance on a certain road. The county maps were collected from the Virginia Department of Transportation. These map layers were what was intersected together to create the areas of interest for data integration and analysis.
Integrating the data presented the greatest issue. Attribute data had to be added to the road map, to provide actual times, in minutes to travel a section of road. Once this process was completed, however, the network analysis tool in ArcGIS was able to build the appropriate network and set up service areas for all seven of the rescue squads combined, as well as each squad separately. The final step in the integration the data was to intersect the current run areas, which were digitalized from paper maps into ArcGIS, with the time lengths presented in each separate rescue squad run area. This created the current run area maps. The service area function in the network analysis provided the proposed run areas.
The analysis of the data showed certain trends. There is, in fact, no doubt that the current run areas of rescue squads are not providing the best and quickest care to the citizens of the three counties. Places like Waverly are spending 30 or more minutes driving to locations less than 20 minutes from Stony Creek and Capron. Surry is driving 30 plus minutes to locations within 10 minutes of Waverly. Surprisingly, however, areas of southern Southampton are very well covered, with almost none of the locations between Capron, Courtland and Boykins having more than a 20 minute response time. The area of most concern is by far the area in the middle of the triangle drawn between Capron, Stony Creek and Waverly. This area, seen in red on Map 2, is currently being covered by Waverly. It is interesting to note, that except for Surry, all of the other stations are actually closer than Waverly. It is simply because of the county line of Sussex and Southampton, added to the traditional lines between Stony Creek and Waverly that continues to leave the area underserved and far less than protected. Simply stated, as it is currently drawn, the citizens in this area are basically guaranteed to have worse outcomes in emergency situations than if a different rescue squad responded to their location. This should be changed, and the proposed Map 3 is the best start.
Understandably, the analysis of time alone cannot be the only deciding factor in changing response models. A deeper study of population densities, call volumes, income and poverty levels, health risk identifiers and other data have to be placed into the conversation. Regression analyses would be the best potential determinant of which combination of variables would be ideal to display graphically on any future analysis of changing response areas and potentially combining EMS systems across county lines. There is also the issue of establishing a dispatch center capable of taking all of the calls, dispatching the appropriate units from the appropriate counties (EMS, Fire and Police). There are significant startup costs associated with combining systems. Finding the best way to pay for the system is complex as well. County tax rates and fees would have to be changed to meet the requirements, political actors would want to t the best deal for their citizens and conflict would arise, resulting in stagnation. It would be no easily established system.
Conclusion
The reallocation of resources is not a new trend. It has been a studied part of EMS for over 30 years. Providing system analyses is the best way to decide what new squads are needed as well as where they should be located. If this analysis is apparently the best way to determine if large metropolitan areas provide for their needs, the same should be done for rural systems. Given standards of care and treatment, along with consistent training within the EMS community, response times to the scene of an emergency is the single most important factor in providing quality care and improving outcomes for patients. The opportunity to improve response, without having to add units and increasing costs, should not be ignored.
References
Mayer, Jonathan D. 1981. "A Method for the Geographical Evaluation of Emergency Medical Service
Performance." American Journal of Public Health 71, no. 8: 841. Academic Search Complete,
EBSCOhost (accessed February 8, 2011).
Sorensen, Paul, and Richard Church. 2010. "Integrating expected coverage and local reliability for
emergency medical services location problems." Socio-Economic Planning Sciences 44, no. 1: 8-
18. Academic Search Complete, EBSCOhost(accessed February 8, 2011).
Tuesday, March 22, 2011
Politics Never Leaving Politics
First I want to preface this post by saying, "Yes, I am absolutely being a sore loser" But I believe I have a right to be one.
Old Dominion entered the Virginia Redistricting Competition. The past couple of posts have referenced the project and even showed the exact map we submitted for consideration in the competition.
I do not believe my fellow group members and I have reinvented bipartisan politics, made the most amazing and unquestionable maps and are above the opportunity for judgment and disagreement from onlookers. We have made a map, plain and simple, that has developed a change in the current representational strategy. In our map, as you can see on this very blog, we no longer have Charlottesville in the same district as a county touching the North Carolina border. We avoid the 3rd district's re-distributive leaps across the James River to make a minority-majority district according to the Voting Act. Our overarching ideas are that people who live east and west of each other in the state have more in common that those who live north and south of each other. I believe this idea is visually apparent. We stressed the ideas of naturally occurring geographic boundaries, such as the James River. We followed that idea with man made boundaries, such as county lines and interstates, should be considered. We remained within the requirements of equipopulation by keeping each of our districts within 1% of the ideal number(727,366), which meant no district is more that 7,000 under or over the magic number (6,200 is our largest difference). Our districts remained as competitive as possible. We had 3 districts within the margin of error for elections, and 5 more of our districts were within the swing vote criteria. Basically 8 of the 11 districts we drew could be won by either party.
Most importantly, we remained consistent to the idea of place. We made maps that allowed counties, cities and even neighborhoods to remain intact, within any electoral cycle. An extreme emphasis was placed on the priority that neighbors voted for the same representation. We never went deeper than the census block level. The idea that two person's living next door to each other voting for different representatives was one we felt should be an automatic dis-qualifier.
This was not the case.
The map that won paid no attention to place. The map that won was a math equation, not a map. It was able to create an equal population down to 100 persons. But this meant it gave up any sense of place. The primary example to this is shown in its minority-majority district. Without showing the map, which I think would belligerantly unfair, I can tell you that to maintain contiguity in its minority district, the team cut a county along the northern most borders. Simply stated, in the district, if a person lived at the end of a cul-de-sac, they had a different Congressional representative than the person who lived at the front of the same cul-de-sac. The two persons live on the same named road, in the same town, in the same county. The children of these people go to the same elementary school, share the same public utilities, mayor, council, police and fire departments. Yet, the winning map decided that the two needed to be split on the most important representational scale, in order to allow for an equal and diversified ethnic population.
Gerrymandering is usually done to keep certain voices silent. The goal of the project was to show that everyone was able to have a voice. But to grossly gerrymander to create fairness is not fair either. It is still gerrymandering and should be treated as such. Yet, this is the map that won.
More than anything, I feel bad for all of the schools that put so much work into the project, to be ignored. Any school could have plugged information into ArcGIS and made an equation. If this was the competition's goal, it should have been stated. I feel the competition would have been ignored though, if the goals were stated as such. Many Virginia university students made good maps, but maps were simply not judged.
Congratulations to those who won.
Old Dominion entered the Virginia Redistricting Competition. The past couple of posts have referenced the project and even showed the exact map we submitted for consideration in the competition.
I do not believe my fellow group members and I have reinvented bipartisan politics, made the most amazing and unquestionable maps and are above the opportunity for judgment and disagreement from onlookers. We have made a map, plain and simple, that has developed a change in the current representational strategy. In our map, as you can see on this very blog, we no longer have Charlottesville in the same district as a county touching the North Carolina border. We avoid the 3rd district's re-distributive leaps across the James River to make a minority-majority district according to the Voting Act. Our overarching ideas are that people who live east and west of each other in the state have more in common that those who live north and south of each other. I believe this idea is visually apparent. We stressed the ideas of naturally occurring geographic boundaries, such as the James River. We followed that idea with man made boundaries, such as county lines and interstates, should be considered. We remained within the requirements of equipopulation by keeping each of our districts within 1% of the ideal number(727,366), which meant no district is more that 7,000 under or over the magic number (6,200 is our largest difference). Our districts remained as competitive as possible. We had 3 districts within the margin of error for elections, and 5 more of our districts were within the swing vote criteria. Basically 8 of the 11 districts we drew could be won by either party.
Most importantly, we remained consistent to the idea of place. We made maps that allowed counties, cities and even neighborhoods to remain intact, within any electoral cycle. An extreme emphasis was placed on the priority that neighbors voted for the same representation. We never went deeper than the census block level. The idea that two person's living next door to each other voting for different representatives was one we felt should be an automatic dis-qualifier.
This was not the case.
The map that won paid no attention to place. The map that won was a math equation, not a map. It was able to create an equal population down to 100 persons. But this meant it gave up any sense of place. The primary example to this is shown in its minority-majority district. Without showing the map, which I think would belligerantly unfair, I can tell you that to maintain contiguity in its minority district, the team cut a county along the northern most borders. Simply stated, in the district, if a person lived at the end of a cul-de-sac, they had a different Congressional representative than the person who lived at the front of the same cul-de-sac. The two persons live on the same named road, in the same town, in the same county. The children of these people go to the same elementary school, share the same public utilities, mayor, council, police and fire departments. Yet, the winning map decided that the two needed to be split on the most important representational scale, in order to allow for an equal and diversified ethnic population.
Gerrymandering is usually done to keep certain voices silent. The goal of the project was to show that everyone was able to have a voice. But to grossly gerrymander to create fairness is not fair either. It is still gerrymandering and should be treated as such. Yet, this is the map that won.
More than anything, I feel bad for all of the schools that put so much work into the project, to be ignored. Any school could have plugged information into ArcGIS and made an equation. If this was the competition's goal, it should have been stated. I feel the competition would have been ignored though, if the goals were stated as such. Many Virginia university students made good maps, but maps were simply not judged.
Congratulations to those who won.
Sunday, March 13, 2011
![Doug Johnson, a political science major at Old Dominion University, is among students participating in the Virginia Redistricting Competition. "I live for this kind of stuff," he said.
<span class='credit'>(Bill Tiernan | The Virginian-Pilot)</span>](http://media.hamptonroads.com/cache/files/images/616011000.jpg "Doug Johnson, a political science major at Old Dominion University, is among students participating in the Virginia Redistricting Competition. "I live for this kind of stuff," he said.
<span class='credit'>(Bill Tiernan | The Virginian-Pilot)</span>")
1 OF 2 PHOTOS:
Doug Johnson, a political science major at Old Dominion University, is among students participating in the Virginia Redistricting Competition. "I live for this kind of stuff," he said.(Bill Tiernan | The Virginian-Pilot)
VA. COMPETITION
Teams from 12 schools submitted plans based on data from the census. Winners’ ideas will be sent to the commission in charge of designing new districts.NORFOLK
Dozens of bleary-eyed college students - many toiling over spring break - have been squinting at computer screens for weeks, meticulously drawing lines on electronic maps of Virginia.
They've been engaged in what cynics might consider a quixotic task: designing proposed legislative voting districts that are sensible, fair, competitive, and drawn with no intent to protect incumbent lawmakers.
There are cash prizes for the teams that draw the best maps. But the big question is: Will the General Assembly, which will create the new districts, pay any attention?
The students are not holding their breath.
For years, would-be government reformers have fretted that the decennial task of remapping voting districts - required by the Constitution to reflect population changes after every census - is a flawed process that inhibits democratic competition.
The problem has gotten worse in recent years, they say, thanks to increasingly sophisticated mapping software that allows lawmakers to design districts tailor-made to ensure their re-election.
In essence, legislators are choosing their voters - not the other way around.
After the last remapping of Virginia districts in 2001, more than 90 percent of the races for the state Senate and House of Delegates were non-competitive - meaning they were decided by margins of victory exceeding 55 percent, according to an analysis by the Weldon Cooper Center for Public Service at the University of Virginia.
Of the 100 House seats, 62 were completely uncontested.
In an effort to boost competition, some states have removed the responsibility for redistricting from the legislature and given it to an independent commission. Proposals for something similar in Virginia have gone nowhere in the Assembly.
Lawmakers are feeling some heat on the subject, however.
At a series of public hearings held last fall by the House and Senate redistricting committees, dozens of citizens begged legislators to turn the job over to an independent panel.
In January, Gov. Bob McDonnell appointed a bipartisan advisory commission to design proposed new districts. Its recommendations are due April 1.
That commission will receive the winning plans produced by the college competition, the first of its kind in the country.
The students had a long list of criteria to meet. They had to draw districts for the House of Delegates, state Senate and U.S. House of Representatives that are fair, competitive, compact, contiguous, nearly equal in population, respectful of city and county lines, and in compliance with the federal Voting Rights Act's requirements for adequate minority representation.
Nowhere on the list was protection of incumbent lawmakers.
"We're going to see a lot of maps that draw a lot of incumbents out of office," said Quentin Kidd, a professor at Christopher Newport University who helped organize the competition. "But they'll be more compact. They'll be respectful of existing political subdivisions. They'll accomplish all the other things that people say they want to accomplish with redistricting."
Kidd said the students are savvy enough to know that the districts finally adopted by the Assembly are likely to bear little resemblance to the college teams' maps.
"But the goal here is as much educational as anything," he said. "For the first time in the history of the commonwealth, the public will be able to see redistricting maps that were produced largely in the open without any sort of back-room dealing going on."
Thursday was the deadline for the student teams to submit their plans to the judges. Fifteen teams from 12 schools submitted entries. The winners will be announced later this month. The prizes could be as high as $2,000.
For many of the teams, including Old Dominion University's, the deadline fell during spring break.
Doug Johnson, ODU's team captain, was in the geographic information system lab in the Mills Godwin Building on Thursday, making final tweaks to the plan before submitting it.
Johnson, a junior from Suffolk, is a political science major who hopes to go to law school. He said he expects the team's plan won't carry much weight with lawmakers, but that's OK.
"I'm a poli sci major. I live for this kind of stuff," he said. "If they take away one good idea from it, then it was well worth doing."
Bill Sizemore, (757) 446-2276, bill.sizemore@pilotonline.com
Monday, February 28, 2011
How might GIS and Geographers help healthcare workers and international aid workers in those countries that require it? In developing nations, 3rd world nations and nations suffering from distinct natural disasters, geographic information can be applied to learn, trend, treat and eradicate health issues. Much of the emphasis in caring for the people that need the help is based in vaccinations and treatment of symptoms once inflicted. By using geographical information, the emphasis of treatment would not change, but could be focused on at risk populations, opposed to the current system of first come, first serve.
A primary example of how geographers can help provide solutions to healthcare crises globally is the first example of it. Rarely is the primary and most explanatory and obvious example of the success of an idea the first documented use of it. In geography, Dr. John Snow’s use of mapping Cholera in 19th century London provided the framework for future uses of geographical health mapping. In the Journal Social Science and Medicine, an article by Tom Koch and Kenneth Denike recreated the process of Dr. Snow using GIS Software and analysis to show how accurate his work was, even given the lack of technological resources available to him. His solution, to simply remove a handle to a water pump, saved the city from consistently rising Cholera deaths, and proved the disease was water borne and not carried in the air, as was the common thought of the time. Geography solved a medical crisis.
Today, articles in the news shed light on the fact that there are still so many health epidemics in the world, especially in the poorest regions, and most particularly earthquake hit Haiti. Cholera in the nation is immeasurably high. Other diseases, completely extinct in developed parts of the world are growing in occurrence and frequency. When the news develops this idea to the public, the ideas of Dr. Snow have to be re-established. Focusing ideas on mapping the trends that have been seen in disease outbreak can be crucial to finding the source and eradicating it before it kills more people.
News sources, which are open sources, are reliable in the sense that they can develop and provide attention to a problem, issue or concern. Deep sources, such as journals, can give the empirical knowledge to help solve those issues. Being sure which is which and how to use them efficiently is key in establishing one’s research and practicum.
Bibliography
Jones, Steve. "Steve Jones: Plagued by old enemies - Telegraph." Telegraph.co.uk - Telegraph online,
Daily Telegraph and Sunday Telegraph - Telegraph. N.p., n.d. Web. 28 Feb. 2011.
<http://www.telegraph.co.uk/science/8309590/Steve-Jones-Plagued-by-old-enemies.html>.
Koch, Tom, and Kenneth Denike. "Crediting his critics’ concerns: Remaking John Snow’s map of Broad
Street cholera, 1854." Social Science and Medicine 69 (2009): 1246-51. Elsevier. Web. 27 Feb.
2011.
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