1.1 Introduction to Location Intelligence

Hi, I'm Rob Williams from Penn State. I'm going to demonstrate how to use geospatial intelligence for a business problem. In this case, where to locate a new metropolitan airfield where we want it, say near a business center, and not where the airports are today. So the analytical problem is, what's the location within an urban area to put a new air service that doesn't need a runway? And I'll talk about that runway in a second. But as I looked through this problem, I discovered that I had to amend the problem statement. Instead of location, I really need to address places. And the place includes where are the people, what are they doing, what's happening in that region? And not just the downtown urban area, but the entire metropolitan region. I also saw that looking at airline service was too narrow. And what really is important is the entire air transportation system, both the private sector and the public sector, and this is now our problem. So what are these aircraft that we're talking about? These are vertical takeoff and landing aircraft. The ones on the left, you can see, are familiar to us. Traditional helicopters, either the small executive class or the larger transport class for people or cargo. And there's a new class of aircraft that could be coming in the future, tilt rotors, which take off and land like a helicopter vertically. But as you can see in the upper picture, the rotors tilt forward and the airplane can fly like a commuter at high speeds. So the opportunity here is, where can we locate these vertical take off and landing aircraft, in urban areas, that adds to the economic wealth of an area? These could be either in downtown areas like the Manhattan heliport you see in the center or maybe out on a ring-road like the Hotel Vertiport located on the right. We're going to look at the benefits to the economic development both from the traveler's standpoint as well as the community and the economic benefits. There's also costs for this kind of service, either environmental, noise, safety, and the cost of infrastructure. But for this particular problem, we're only going to focus on the benefits, using our GIS methods. The value then would be what's the value to a traveler, either by shortening their travel distance on the ground, making it more convenient, there's also benefits to the business trade areas, and finally, the overall economic activity. These can all be evaluated using Geospatial Intelligence methods. The specific methods we use are business location analytics and network analysis. A tool suite which is good for this purpose would be ESRI's Business Analyst Online, and they also happen to provide a lot of the data. I'm going to speak specifically about this very interesting set called Tapestry. So the overall approach is three steps. We're going to look at a sample metropolitan area. We're going to do a coarse evaluation to figure out where in that metropolitan area might we want to locate such an airfield, and we do use this through either direct measurements of data or maybe proxies, we'll create the data layer maps, and then pick a general target area. And then, finally, using fine tune analysis, we'll look at the benefits of specific spots on the ground, specific places, and determine which is the best. So, to start off, the sample market I was looking for: a urban area, with a single airport, that had lots of good data about the demographics. I selected the Philadelphia region for this purpose. So, in the Philadelphia area, one of the first things we can evaluate is who travels by air? ESRI and the online system has a data set for exactly this. Who has traveled more than three times in the last 12 months? And it's divided by zip code. So, here you can see in the green spaces, households by zip code that have traveled three times or more in the last year. Philadelphia airport is at the airplane icon in the center, so you can see north and west of the city is a large density of travelers and then to the far northeast up near the Trenton area, you can see another center. We're going to focus on this area north and west of Philadelphia Airport. To do this, I'm going to use the Tapestry data set. This is a really interesting dataset that looks at demographics, population, wealth, income, types of jobs, types of mobility, residences, and, in particular, this group called the affluent estates on the left. This is the wealthiest group, and the premise is that these folks probably do the most travel and would be most interested in having this kind of service available to them. So, looking again at that map of Philadelphia with Philadelphia spotted in the center. Again, north and west of the city, in red, in that circle, you see zip codes in red that have the highest density of the affluent estates residents. So that is an initial indicator. Second, I also looked at US census data, the NAIC code for finance and industry or insurance industry, and you can see in that same region, just a sample, but you can see how dense that same area is for these kind of businesses. So, this area north and west of Philadelphia airport looks like a good place to look more closely. So, as we hone in, I picked one spot in the center of that region. And now, it's a question of where to put that first initial look. I'm looking for, in particular, short driving times, ten to fifteen, ten minutes or so, and, in particular, I'm looking at who lives in this region? What kind of residents? The blue and the yellow colored regions are the highly mobile people in the Uptown Individuals and GenXurban from the tapestry data set. They would probably be most interested in having this kind of air mobility right in their neighborhood. But more interesting is the green and the orange segments in the surrounding areas. These are the folks who have, say ten to twenty minute drive times to such a location. And this is the population we're very interested in. So, MIT did a study on which industries have the highest propensity for air travel compared to trains, compared to automobile. And, you can see the industries here that use the air transportation the most. So, now that we have identified these industries, we can use US Census Data, the North American Industry Classification System to actually identify on maps where these folks and businesses live. So, wholesale trade, finance and insurance, and professional services, we'll focus on those three. In the first case, wholesale trade, you can see in three different maps whether the number of businesses, the number of employees, or sales by ZIP code, you can see this concentration around that blue dot that we selected for this study of where to site a new airfield. This looks like a nice concentration right exactly where we placed our marker. In the next industry, finance and insurance, again we see the same pattern. Number of businesses and employees. This again looks like a good segment. Finally, we verify it. A third segment, professional services. This looks like a good pattern. So, we're happy with where that dot is located. We're going to now look more in detail. So, three sites were selected for detailed analysis. These are three areas that are available for development. They're all in that same general area, but which one is the best? So Riverside, King of Prussia, Norristown. We're looking again; these are all three far from the Philadelphia airport. But we're going to look at these 20 minute drive times. If we go back to the tapestry data, there's a different look at the same population, and it's called the urbanization groups. And these are the concentrations that we're looking for. Where is the densest groupings for the same US populations? We already identified the affluent estates. So, this is one group, but they may not be the only group we're looking for. There's also people who are upwardly mobile, who are going to want to travel, who are rising in their careers, these folks too would want to be located near this kind of air facility. So we're going to look at these population groups in the tapestry. So for a specific analysis, we're looking at 10, 20 and 30 minute drive times. From either the Philadelphia airport or these three possible airfield sites. And as you can see in the 20 minute drive time, both the riverside and the King of Prussia look like they have the best collection from these population groups. The 30 minute drive time, you have over 100,000 households that are possible users of this air facility. You'll also notice that Philadelphia Airport increases as well, but my belief is that those people actually live in a different side, perhaps down in the Delaware and the New Jersey region. So I think these three sites are still very good. So then, the final analysis, we see the 20 minute drive time, and of all the different demographic groups, it seems like the majority align best with the King of Prussia site as having the largest numbers at the shortest driving times. So, I think the King of Prussia site is going to be our choice. So, in conclusion. Using the Geographic intelligence analytic methods seems to be a very good way to analyze this particular problem. We used a sense of place, meaning what's the travel propensity and the household incomes and the types of travelers. Seems to be adequately covered. The coarse site selection yielded our general location of where we wanted to site the airfield, and then the fine analysis, we actually could figure out exactly where we want the locations and what is the best site. Thank you.