00:01I want to start today by showing you some of the enhancements to network analysis in ArcGIS 10, specifically location-allocation.

00:09In the Angeles National Forest, just six months ago, the Station Fire burned 250 square miles of forest.

00:18Not only did the fires themselves pose a major threat to life and property, the burned mountainsides now pose a greatly increased risk of mudslides and flooding in the rainy winter months.

00:31We're especially concerned with those areas where mudslides and flooding could lead to a loss of life and property and potentially to evacuations, like this area just north of Pasadena.

00:44What we want to do is plan for evacuations in the potential mudslide area.

00:50We know the population that will need to evacuate, and we have 36 potential locations for evacuation centers.

00:59What we want to do is choose the best evacuation centers so that we can minimize the travel time for all of our evacuees.

01:08To do this, we're going to use location-allocation, which takes as its inputs those evacuating populations and our 36 potential locations for evacuation centers.

01:22I'm just going to set some of our analysis parameters starting with the problem type.

01:27We'll choose the Minimize Impedance Problem Type, which is going to allow us to minimize the travel time for all of our evacuees.

01:35We also want to choose five facilities.

01:39So this analysis is going to choose the best five evacuation centers that will minimize the travel time for all of our evacuees.

01:48We can also use location-allocation to include some of the real-world challenges that would be faced in an event like a mudslide.

01:56In this example, we've gotten word that there's been some flooding in the area that's going to slow evacuees down as they make their way to safety.

02:05We can very simply include that information in our analysis by rerunning location-allocation, this time including some barriers.

02:15Now to include the barriers in my analysis, all I have to do is draw some polygons on the map.

02:22The ability to use polygons as barriers to network analysis is another enhancement in ArcGIS 10.

02:30Now these polygons don't represent strict barriers to travel.

02:34They only represent an increased travel time associated with those areas.

02:39So I'm going to go in and use an attribute to represent that cost of travel for each one of those polygons.

02:47And then we want to rerun our analysis. Except this time, we're going to include a 15-minute travel time cutoff.

02:55So this analysis is not only going to ensure that we've minimized the travel time for all of our evacuees; it's going to ensure that nobody has to drive for more than 15 minutes.

03:04So this way, we're able to ensure that we're providing these services equally and responsibly to all the citizens.

03:13So now we can see that the evacuees are being directed to either side of that flooded area, and our analysis has taken into account those new parameters and chosen five new locations for our evacuation centers.

03:26So we're able to use the enhancement to network analysis in ArcGIS 10 to choose the best locations for our evacuation centers and to ensure that we're providing those services responsibly to all the citizens.

03:40Now I want to move on to a topic that I'm really passionate about and, frankly, it's something that impacts all Americans, and that's childhood obesity.

03:50Right now, one in five children in the United States is obese and 80 percent of those children will become obese adults.

03:58This has major implications for health in the form of heart disease, diabetes, and a host of other health-related issues, not to mention the economic impact that this issue has on the United States.

04:10Medicare says that they spend over $9,000 a year more on obese individuals.

04:15So this is a huge problem and something that we need to understand better so that we can start to think of some potential solutions to the problem.

04:23What we're looking at here is school-level data in Los Angeles County, and we have the percentage at each one of these schools of fifth graders that are obese.

04:34Some of these schools, 75 percent of fifth graders are obese.

04:42We can use spatial statistics to run a hot spot analysis which will show us where we have spatially statistically significant clusters of childhood obesity…

04:53…which will help us understand where the problem is and how we can focus our resources on those areas that are being the hardest hit by this problem.

05:04So immediately we see that there is definitely a spatial pattern to this problem.

05:08There's a cluster in the middle of our map, and that's what's known as South Central Los Angeles.

05:13We can see that to the northwest and the northeast, in wealthier areas like Hollywood and Beverly Hills, childhood obesity is not as severe of a problem.

05:23But in South Central Los Angeles, it is a huge problem, and there is a definite spatial pattern.

05:29So what's causing this problem?

05:31Well, for one thing, we know these kids aren't eating healthy food.

05:35They're definitely eating fast food.

05:36There's tons of it practically on every corner, so no shortage there.

05:42What about healthy food, fresh fruits and vegetables, the staples of a healthy life?

05:47Well, some of the only places to get healthy foods like fresh fruits and vegetables are at supermarkets.

05:53And it does look like there are pretty much supermarkets everywhere, although it looks like they're a little sparse in our major cluster of childhood obesity.

06:02We can use another tool from Spatial Analyst to create a density surface, which will show us where we have higher and lower concentrations of supermarkets in this area.

06:12So immediately we can see that there's a very low concentration of supermarkets in our major hot spot area, which really represents a gap in the accessibility to healthy food.

06:25We're also interested in who is being impacted by this problem.

06:29We've created some standard deviation ellipses that show us the distribution of different ethnic groups in this area.

06:36And we can see the two ethnic groups that directly overlay with the hot spot of childhood obesity.

06:43So not only do we have an issue with the accessibility of healthy food, we have a problem with the equity of these services and how they're being provided.

06:52So how can we start to think about the potential solutions to this problem?

06:57Well, for one thing, in Los Angeles County, there are hundreds of small corner stores and convenience stores that currently only sell nonperishable food items…

07:06…canned food, junk food, and a lot of families rely on these stores to feed their families.

07:14We can use tools like location-allocation to help us decide which of these small local, already-existing food stores…

07:21…to incentivize and subsidize the sale of fresh produce, fruits and vegetables, so that we can start to close the gap in the accessibility of health food.

07:32And I'm sure you're asking, Well, how are we going to pay for all of these subsidies and incentives?

07:37Well, the federal government is currently spending billions of dollars on our economic recovery, and quite a few of those dollars are being spent right here in Los Angeles County.

07:48So now that we understand where the problem is and who's being impacted, we can start to direct some of that funding to the areas that need it most.

07:57So we're able to use spatial analysis to understand the significant problem of childhood obesity, some of the related causes, and the potential solutions that can start to really change the future for these children.