(Unedited) Podcast Transcript 338: Better Data, Better Planning

This week we’re joined by Alex Hoffman, Assistant Director for CID Planning at the City of El Paso. We chat about using data to make planning decisions, the geography of place, and what the future might look like with more information.

A full unedited transcript of the episode is below.

Jeff Wood (1m 32s):
Alex Hoffman, welcome to the Talking Headways podcast.

Alex Hoffman (2m 3s):
Thank you for having me, Jeff. I’m looking forward to today.

Jeff Wood (2m 5s):
Yeah. Thanks for being on the show. Thanks for listening to this show. I know you’re an avid listener before we get started. Can you tell us a little bit about yourself? My

Alex Hoffman (2m 11s):
Name is Alex and I’m with the city of El Paso and El Paso, Texas. I oversee our capital planning and a longer range as a part of our capital improvement department. So I worked with a bunch of engineers about, we get to do to a really cool planning work and doing a lot of different planning studies and working quite a bit hand in hand to do everything from project selection, to using data, to help inform different decisions the city’s making and to provide long range projections. And how did you get into planning and to cities? Yeah, so I went to school for planning. So I’m a graduate of the university of Cincinnati. I went to their school planning and then I actually have a master’s degree on that also in the planning from a lot of state.

Alex Hoffman (2m 55s):
So I’ve been a lifelong planner. I love cities. And I trying to figure out like, what is a way that I could have an impact on them, the way that cities look. And it just because when you think about leaving behind a legacy in the built environment is a pretty substantial and it’s pretty permanent. I know things change obviously, but you know, it’s a pretty big way to leave behind, you know, your imprints. And so just my love for cities and wanting to have a positive impact on the way that they look, I was like, wow, because we get into planning. And so I’ve been in practicing planning for about 15 years. I’ve been at the sea of a possible for about 10 of those years.

Jeff Wood (3m 35s):
And how is El Paso different from say like Columbus are, or even the Cincinnati? Oh,

Alex Hoffman (3m 40s):
That’s a really good question. So, I mean, I think, you know, the, the, the biggest thing, it’s just geography at the location. So it’s a very spatially isolated, you know, the nearest large city from El Paso is Albuquerque, which is about 300 miles away. And when you think about the 300 miles away and Columbus or Cincinnati, you’re hitting another major city. And so just, I would say to the spatial isolation is a really big driving factor on what we’re able to do and then sometimes not do as it relates to the planning and our community. It’s good though, because

Jeff Wood (4m 13s):
You can go to the west Texas and get some really cool starry nights too. Yeah. Yes, naturally. Yes.

Alex Hoffman (4m 19s):
So you do have that as a buffer, as a benefit.

Jeff Wood (4m 22s):
Yeah, for sure. I have driven through a pass on a few times on my way to California or to Colorado from Austin and a I’ve always loved my way through it. You’ll see the, you know, just the, the, the mountains and it’s pretty. And then you have the, you know, Juarez across the way how’s the city is a relationship with another kind of large city on the other side. That’s separated by the board. Yeah. I mean,

Alex Hoffman (4m 41s):
It’s kind of interesting. So I know that, you know, a Passover is a relatively alert city, a I know, but the last time that the census would tape and I think we’re at the 19th largest city, but just being able to have the sister city that has, you know, a 1.2 million people it’s substantial. And the only thing that separates it is this arbitrary line. And so I would say that our relationship with, with one of the us is significant. And it’s just because even though I think for people who don’t live near the border, there’s always the thought that, you know, it’s not poorest, but what we experience on a day-to-day basis is that we see that people travel everyday to do very basic things, whether it’s going to school or going to work, or even, you know, where they see their families on a regular basis.

Alex Hoffman (5m 27s):
And so even though there’s this line that separates them, there’s significant overlap. And the things that we do everything from, like, how do we connect our ports of entry, so, or a bridges, we sometimes coordinate project on both sides, but then also the city meetings to make sure that when we think about providing for services, whether it’s like roadways and those types of things that we consider this population, that’s not even like a resident of a country let alone our city, just because they do have a tremendous impact on or infrastructure just with, you know, people coming over for the weekend or just going shopping. And so we to see this massive influx of non El Paso residents every day. And so it is an important part of what we do as it relates to the planning here locally.

Alex Hoffman (6m 10s):
Are there any

Jeff Wood (6m 11s):
Other instances that you kind of take in, you know, best practices from, because of that? I mean, I imagine like Tijuana or even other countries on north of the board, or, I don’t know, I’m just curious if there’s like, you know, a sharing of information between kind of border cities. Yeah.

Alex Hoffman (6m 25s):
So, I mean, I think that it’s like anything else, right. I think that a lot of times you’re looking for case studies of successful projects and, and other places. And so I, you know, you, you mentioned Tijuana and San Diego. I mean, I think that, you know, in terms of different things we’ve done, especially as it relates to planning the area’s around the bridges, we do look to those cities to help me, you know, maybe give some guidance in terms of what best practices our, but the reality is is that the region is so unique that, you know, at the same time, we don’t really have a benchmark to compare ourselves against the other border. Cities in Texas are significantly different than what we see here.

Alex Hoffman (7m 6s):
And, you know, were moving a lot of truck freight, for example, whereas, and Tijuana and San Diego, there’s a lot more, you know, the, the, the water ports. And so it’s, it’s even different freight, different circumstances, different situations. And so it is a challenge. I think we’re kind of on our own there sometimes in terms of figuring out what the right thing to do when it comes to planning and coordinating with other members of the south. Yeah. The such

Jeff Wood (7m 31s):
An interesting connection, but I wanted to chat with you because you posted an item on, on LinkedIn that I thought was fascinating. And I had read a research piece from brown university where they looked at a 133 million tweets and found that people just aren’t segregated at home, but they’re are segregated by were they visit and where to eat, recreate shop, to socialize all those places. But you have been looking at these trends using data as well. I’m curious, what put you on this path and kind of, what are some of your findings from looking at the data? Yeah, so,

Alex Hoffman (7m 57s):
I mean, I would say, you know, what kind of put me on this path towards data and data analysis is just to seeing, you know, in other fields where data has become really central to the practice. And one of the things that I’m starting to see it as a trend, I don’t think within the field of planning that we’ve fully realized the potential at this point is utilizing data to make decisions. I think that we’re starting to see some significant end roads as it relates to transportation planning on some of the transit stuff, just because of micro mobility and some of those types of topics. But I think we’ve really just scratched the surface for planning because sometimes the things that we talk about or are so complex, they’re so challenging.

Alex Hoffman (8m 38s):
And we haven’t ever really been able to, I guess, use the facts and data for that matter to help support the positions that we have. And so I think part of it was seeing that it’s a trend and other fields wanting to, you know, really get versed and the information four, the context of planning. But then the other thing too, is just, I think during the pandemic has more time to time and home. And so I was like, well, you know, it’d be really helpful if I taught myself how to do some data analysis, data values. And so I really just started using the opportunity to take the time to, to teach myself how to do it. And so that, I think that’s really what set me on the path of, okay, how do we use data more to make our decisions?

Alex Hoffman (9m 21s):
So

Jeff Wood (9m 21s):
I’m always curious on this question is what is data? I mean, what does that mean? What does the data that you’re using and why do you see it as helpful in your, and your move to a kind of use it more for planning and not just say a transportation? Yeah.

Alex Hoffman (9m 31s):
So, I mean, I think when I say go or what I mean typically is, you know, I like to split data into really, to buckets. There’s like more of the traditional data sources. So like the things that are obvious that people already use and that’s like the census data. And then there’s like the other non-traditional things, which is like, where do you scrape data off of a website? You repurpose it for something else that doesn’t really have, you know, its intended purpose. And so I think that I’ve been looking at to combine those two things where we, we use like these traditional or nontraditional all the data sources to help, to answer certain questions. I would say that the majority of the data that I’m typically dealing with is more of like your origin destination data.

Alex Hoffman (10m 12s):
And the reason for that is just because I think it gives insights into things, some macro trends that are happening within the city that are things that we always thought, maybe we were a true, but we never really had information to help support it. And so for me, one of the things that I’ve really been trying to do is to think about, okay, how can we take this particular data source? And let’s just use, or origin destination data as an example, how do we use that data source and what type of insights can it give us and to maybe how people are consuming the city, just in the broadest sense possible and what other types of patterns can it reveal and you know, what, to your point that’s were, you know, we have the opportunity to look at, you know, what other types of segregation is taking place in the city, because we traditionally just think about it with housing, but we can use to origin, destination data to provide insights into, you know, these other segregation patterns where people spend the rest of the day.

Alex Hoffman (11m 8s):
And one of the other things that I’ve used the same data and information for is like how to people access city services. So our, we also being equitable in the way that we’re providing parks or are they equally accessible, same thing with libraries and senior centers and rec centers and these, or just things that are traditionally, we haven’t been able to provide insights into that now because of the relative ease with which we can access this information. Those are some questions I think that we can start to, to look into and see, are we doing things the right way? Because I think we always have the best intentions as planners, but obviously a history has, or a real that sometimes we have some very serious consequences and some of the decisions we make when we don’t think about things holistically and we don’t use data to support or decisions.

Alex Hoffman (11m 53s):
Yeah.

Jeff Wood (11m 53s):
And you use the tool called safe Graf to look at, like you said, the equity of destinations, what does the inequity look like? And what does that lead to in terms of, in the data? And what does that mean in terms of potential solutions? I mean, if you look at this data, what can you kind of figure out about whether there are people are going to parks or libraries or not? Yeah. So

Alex Hoffman (12m 11s):
I mean, accessibility of just say all of the points of interest throughout the city, you know, I think the thing that really stood out to me that I think is just a really, really important reminder, is like, when we talk about things like walkability as a general goal that we have, we have to keep in mind that just because there are a destination nearby doesn’t mean that there are accessible to people. So like, as an example, like I know that whole foods get the rapid being a whole paycheck. And so like, just thinking about like, when we think about food deserts, like our, the places that we you’re saying, oh, look, there is a grocery store here, but that doesn’t mean that’s accessible to all people. And so I think we really need to be careful when we talk about accessibility, just in general, a lot of different services throughout the city, I would say than the other thing is that when we were looking specifically at the city services, the thing that really stood out to me was a couple of things.

Alex Hoffman (13m 4s):
So one is just the geography of the way that people consume study surfaces is not equal across all areas of our city. So like as an example, we know that people who live like on our east side of our city traveled significantly further to get to these different destinations. And that provides us some really important insights. Like why are these people driving or, or, or taking the bus and walking so far just to get to these different services? I think the other thing too is then just when we think about the demographic profiles, because from that information, like we knew like their home block group. And so we knew that these areas where, where we have high concentrations of low to moderate income populations, that’s an example that like the type of people who access to services are very diversion based on the facility time.

Alex Hoffman (13m 48s):
And so one of the things that we found again, and it’s something that I think that maybe a research has pointed towards is being a potential outcome. As a, as an example, like are recreation centers are not traditionally used by our low income residents. And what does that mean? And what services do we need to be providing that maybe we were thinking we’re doing this, this great service we’re providing parks. Well, people who live in these areas, don’t, don’t use them. And there could be a number of reasons why are they don’t use some of them maybe because of the hours they work, or maybe they’re just, there is an interest. And they would really like to have a senior center, but the senior centers like super far away. And so I just think that, you know, when we start to look at who the demographic profiles are, who the users are within these different geographies and trying to apply as many different filters to things as possible, it can really help us to answer this question, like who are we serving?

Alex Hoffman (14m 38s):
And are we doing a good job of it? And I just think that that’s not something that cities traditionally do, or if they do it, they don’t do it very well. I don’t think to ask the right questions and maybe they didn’t

Jeff Wood (14m 49s):
Have enough information before. I mean, you have all these data sets of where people are, where they’re going and you can break it down all by incoming. I always excited. And I talked about this on a show a lot, but I was excited and back in like 2003 or so, 2005, when the LHD data came out and you can start to see these origins and destinations and actually map them. But now you have all these companies that are doing this with a cell phone data, and even like a greater kind of precision than, than even that. So it gets really, you know, even more dense when you get to down to that fine grain. And it really speaks to that push for the 15 minute city as well, where we have this push, but we have the data that tells us maybe the 15 minutes. Isn’t quite what we think it is. Yeah,

Alex Hoffman (15m 29s):
Exactly. And so, you know, I, when I think again about, you know, the example of just looking at the accessibility of different businesses, when we think about the 15 minute city, it is about providing all these different types of services within this catchment area. But recognizing that there are no income restrictions as to the different locations and whether intentionally or not in the same way that we see it with housing. And so, you know, these different locations are not, they’re not all created equal. And the data shows that there are places where only people who were visiting them or a high income groups, or the only people visiting them, or a low income groups. And it’s not across the board. And its a really important consideration AZ as we start to, to, to try and implement some of these, a very good planning concepts, but it needs to have that basis of reality, which is where I think the data has the opportunity to take us.

Jeff Wood (16m 18s):
I was looking at the MIT Atlas of inequality because it has a number of different cities around the country that they focused on. And I know that you took some inspiration from that, but I was looking at it and I was looking at my neighborhood and I was interested where, you know, cause they showed the dollar sign, the to dollar signs to the three and the, for a dollar signs to the show who’s going there and, and what the, almost the Gini coefficient of whether it’s equitable or not based on who is going to the store. And it’s interesting to see the, the, the places where people of high income are going in the neighborhood, which is mostly real estate agents and the place where we’re more, you know, kind of everybody, you know, goes to, which may be the burger joint. So it’s interesting to take a look at your own kind of a neighborhood and see who can afford to go where and what that means for, you know, connectivity and access.

Jeff Wood (16m 59s):
It’s really fascinating. Not really

Alex Hoffman (17m 1s):
As, yeah, exactly. That was a project that I would say I borrowed very heavily from because there was a, or was, it was a very fascinating. And I think that, you know, it’s something that can be reproduced because I think, you know, conversations that you’re, and I had a, it’s like the way the studies are done, the traditionally only look at the same cities. And so recognizing that these issues exist outside of these, maybe like very hyper, you know, income, segregated areas. It’s very true. And a place to call Passover where we don’t have the same issues of income inequality. And the difference between the high income earning courts, Powell is not that much different than the low-income. So it’s a true across the board. And it was really interesting to see that in this Metro to, so was

Jeff Wood (17m 41s):
There anything that kind of popped out at you when you’re doing the analysis where it’s like, oh, well that place is kind of creating an aura of this. Why is that? Why is it more, high-income a, why is it more moderate or why is it, you know, I would have thought that this is this, but it’s actually this. Yeah. So I

Alex Hoffman (17m 56s):
Mean, I think one of the things that was really surprising to me was that I was expecting maybe places that, you know, traditionally get like a rap of being maybe a place that only, you know, say for example, like low income people go that they were actually some of the most equitable places. Like I’ll give her a really a specific examples. Walmart I was thinking and a Walmart really gears itself towards being a, you know, affordable at the same time, we saw that there are just as many people in the higher income bracket using that as people who are in the low end. So the distribution was a pretty even across the board, you know, I think the other thing to that, that I saw, or that was pretty interesting was that the places that were, you know, accessible or are not accessible, tend to be clustered together, which one we think about geography as a place that makes a lot of sense that, you know, these areas of a city that are like exclusive that those businesses tend to cluster together.

Alex Hoffman (18m 52s):
Whereas the ones that were maybe more accessible were also more frequently clustered together. I think the big exception was maybe the long are a major streets where you see a lot more diversity have business and business type, but that there were a very significant areas of clusters. And especially within the, the neighbor of a context and it was almost like the business’s and the accessibility to them, or a reflection of the housing that was surrounding it, which makes sense to me, it’s the people we live close by. That would be frequent. Yeah. When you

Jeff Wood (19m 20s):
Show these tools to other folks, what does the response to them and what does the response when you post them apps and when you share it with folks in your department,

Alex Hoffman (19m 28s):
So usually two different reactions. So the ones that are not within my organization, I think that people are like, oh, you know, that’s, that’s really interesting. That’s really insightful. But I know that with a lot of government agencies, that there are a tends to be general, I want to say a reluctance, but like just cautious being cautious about data and the implications that has. And that’s true, but just data science in general. But I do think that there’s a lot of skepticism within government about what data means or we’re making the correct insights based on this information. And so I would say that its not the same level of excitement people or like what am I looking at?

Alex Hoffman (20m 12s):
But I showed them cause the, you know, it is on, is a complex. It is a lot of information to try and share within a single graphic. And then also making sure that I can explain it in a way that a person who doesn’t deal in the data can understand. And so I do think that there’s a split between the two, but there has been a real interest within our organization. And how do we use data to make decisions? And this is just one of those ways and it’s a learning experience for me too. We were making sure that I’m effectively communicating the right message and presenting the information in a way that hopefully some biased as we have the data, it has the, to be biased depending on who’s analyzing it. I used to

Jeff Wood (20m 50s):
Get yelled at well, not yell that out, but you know, a little bit of a, you know, side face when I’d show a map and I had to explain it too much and it just didn’t make sense from looking at it. And so I feel like that’s part of the process is kind of honing the craft of explaining or at least designing the data so that that people can understand it. As soon as they see it, they’re like, oh, this makes sense. This is the thing, but you’re right. I mean, sometimes it’s more complex than you can even kind of simplify it on a map which the nuance sometimes gets lost. Unfortunately. Yeah.

Alex Hoffman (21m 21s):
What a, so it’s something I want to try to keep in mind is if I, if I’m publishing something and I’m doing a descriptor in the script, there’s more than one sentence. I’m not doing it correctly because you know, people are going to continue reading past a certain point. So it is, it’s really important to be a, you know, effectively communicating the same way to do with maps. What does the data to come from that? Your working on specifically? Yeah, so we use a combination of data sources. So with the same graph data. And so they were a really gracious during a pandemic, the high to the pandemic, a that they gave the government agencies access to them, the information that was something that since then has been concluded, but they gave access to their data for about a year. But the other tool that the city uses as well is a tool called replica.

Alex Hoffman (22m 4s):
And so replica is a platform that utilized is similar data sources at help to create a replica population. That’s the name of a replica of how people move throughout your region and gives insights and the things like, you know, a demographic profiles have those individuals utilizing the census data. So it’s not necessarily a like for like match of the person, but it’s a representative population of the people who live within your community. So that’s in the tool that we’d been utilizing most recently a frequently and or something that we’ve had with them for about two years now. And how

Jeff Wood (22m 37s):
Has that, I mean, I guess you’d call it a synthetic data and how does that kind of get ground truth to a certain extent? I mean, I think that there has been questions about whether, you know, a synthetic data can be used in lieu of, you know, the actual movements that people, but then at the same time, the actual movements of people is a privacy concern too. So that’s one of the reasons why I imagined that you would try to create a synthetic population. Yeah. So

Alex Hoffman (23m 0s):
I think that, you know, one of the things that I think is really important to keep in mind, especially when you’re creating the synthetic population, is that it’s really dependent on the user agency to be providing them with the most ground-truth data that you possibly can because of the more that you’ve provided to them, a more accurate the model can become. So like as an example, one of the things that we provided to them was a bus data. So if we have automatic passenger accounts and so we can actually show, you know, here are, or where people are onboarding off boarding on our buses, you know, a similarly we were giving them traffic counts. So that way the data that they have, they can actually compare against what the ground truth data is showing on our side of the terms of how many people were actually using this route during this particular period of time.

Alex Hoffman (23m 44s):
So that way, when they see, okay, we have, you know, a representative sample of like 5% of the people in the city, this is how you can extrapolate out that data and make some reasonable assumptions. Again, it’s like the same conversation that we’re having about when we’re representing data through a map, you always have to make sure that your asking the model, the tools, the right questions. And so sometimes like people really want to get focused on like this particular roadway or this particular segment, and really understanding that as you go in with like this really fine tooth comb, the accuracy does degrade and you need to recognize that. And that’s just, you know, the reality of the situation, especially when you’re trying to balance it against privacy concerns.

Alex Hoffman (24m 24s):
But when you’re looking at and say, geographies of origin, destination, between to block groups, the fidelity or the data is significant and you can provide insights. And one of the ways that I think is confirmed, just because we know a certain information to be true, you know, like you mentioned the LHD or maybe from other data sources where we are seeing these patterns, and then we see this other tool that’s, you know, maybe using similar or different data sources, that’s confirming that same pattern that gives us a certain level of comfort to know that we can make reasonable inferences from the data. If we ask it the right questions. And we look at it in the right geography. Yeah. I was always

Jeff Wood (25m 5s):
Frustrated with LHD. They gave you information to the block, but they were like, don’t use it at the block. Always use it, the block groups or at the census tracks. I’m like, you can’t do anything in the census tracks is Worthless. Exactly.

Alex Hoffman (25m 16s):
Or like, especially when it goes across a major roadway and, or like the difference between like the north side, the south side is a significant, so yeah, I mean, that is a real challenge, but it, the tool a tool has worked really, really well for us. And so it’s just something I think that, you know, especially as the data starts to continue to evolve and get the better, I think is really exciting to be able to have something where we can actually interact with that same data source. We used it for any specific planning purpose already. Yes. So we’re using it for a few different things. So one of the ways that we’re using it is to look at, you know, or are most travel roadways when we make decisions about, you know, which roadways and or city or getting like repaved to re reconstructed.

Alex Hoffman (25m 59s):
A decision that has been made was that we were going to be using are most tribal roadways to rank those in terms of prioritizing. And so that was a way that we used it. We also have to use that information, but we were doing a number of different climate studies right now. So we’re looking to update our downtown plan and then also looking at one of our corridors. And so to help support those efforts, we’ve been utilizing that data to provide insights into, you know, how are people currently interacting with these areas? What does the modal split? What does the demographic profile of the people who visit or who, you know, or going here for work or whatever the cases, again, to provide a really good at appropriate context to the study itself.

Alex Hoffman (26m 39s):
And then one of the exciting things about the tool is that, you know, they do continue release new seasons. And so as we make these improvements through a project or we’re all, so as we do start to implement these plants, we’ll be able to actually see then how a population response to the investment that the city is making. And so it’ll be a really interesting to see what takes place as we continue to get those new data sets as we progress some to studies or one of the interesting

Jeff Wood (27m 4s):
Things that I’ve found from your S your service data, is that the co located services to get used more. I mean, I think this seems obvious, but at the same time, it’s interesting to have that backing information to tell you that that’s the truth. No, certainly.

Alex Hoffman (27m 16s):
And, and, and like you said to me, but it seems like a dove, of course, you have a more than one thing that you’re offering and a place, of course, the more people are gonna come out, but it doesn’t have to be that way. And I think that’s the thing that I always try to keep in mind is even when you’re making these graphics or you’re coming up with, you know, the insights, the key insights that you have, that even an obvious one is still a really helpful, because you’re able to show that this, the thing that you always assume to be true is true. And then Mrs. The degree to which it’s true. So, you know, to, to give another insight that we had is that we know that, you know, there’s always people or so I’ll pass, or like all these other Texas cities that just likes to like annex everything that’s around it. And so as we’ve grown in a land area, we know that a, one of the things to be true is that the county doesn’t offer the same level of service.

Alex Hoffman (28m 6s):
So if the city does so, and so we have always assumed like, oh, we know that the county is using are different facilities and what we thought a true, it was really helpful to see that like, oh, here are people who live in the county and these are the block groups that fall on the county so that we can know with relative certainty that yes, that’s true. And it was like 9%, you know? And so we can say like, yes, we do have the users, the county, but it’s a 9% of the users of, of city facilities are from the county. And I just think, you know, that there’s gotta be for other cities insights like that, that would be really helpful to help frame the discussion around whether it’s for city services are something completely different. And again, it’s just an insight that may seem obvious and maybe like, well, of course, that’s true.

Alex Hoffman (28m 48s):
But just knowing the magnitude to which is true is something that we haven’t known before. Now, what

Jeff Wood (28m 52s):
Does the county do when they find out that like 9% of their population is using city services are, what does the city do as to say like, all right, pony up, you know, give us some more money because we know that you’re a population is, is using some of our services that they’re not maybe paying taxes or

Alex Hoffman (29m 7s):
Right. That’s like the it’s always, the dangerous thing is being the person who’s using the data because it’s like, is my data going to be used for good? You know? So it’s like the, as you’ve made these insight, you’re like a God, I hope that they don’t see this and think like, oh, we should charge a fee. You know? And that’s always, that’s always the concern. And I think is another thing you should be mindful of as you, as you put these things out, it has to like, make sure that maybe the insights that you’re releasing can be insights that are used for good, the good purposes, because yeah, I mean, not being the ultimate policy decision maker, I think can put you in a precarious position because you’re providing these insights and not really knowing, okay, the decision maker we’re like, or council, or like, what are they going to do with this information?

Alex Hoffman (29m 51s):
It can have a ramification where you had a really good intent when you did it, but now it’s been used in a way that you didn’t originally anticipate. And it has a negative consequence. I think that is something that is going to be a challenge with using data moving forward, especially within a planning context, because they are typically really important policy decisions and not knowing ultimately like how the information is going to be used. It can be problematic. Have you seen

Jeff Wood (30m 19s):
Any of the unintended consequences happen already or is it something that’s in the future that possibly could happen?

Alex Hoffman (30m 24s):
So, I mean, I think for us, I think that we’re still pretty early on and, you know, how do we use data to make decisions that I haven’t really seen anything be negative a at this point, but I do think that, you know, I’ve seen instances in like other cities where maybe they’ve used data, that was a data for good for, you know, may be a political purpose for some other type of thing that really it wasn’t, you know, intended for. And I think that’s why then it goes back to this question of data privacy. Cause the last thing you want to do is, you know, you’re doing this study and you’re like, we want to research something and that, you know, or maybe is for making sure that there are, you know, there’s racial equity or that there’s, you know, income equity or whatever the case is.

Alex Hoffman (31m 12s):
And so you need this different information and then the information gets in to like a different administration or someone who maybe doesn’t have such an altruistic purpose in mind. It can really, I could see how it can very easily spiral into something that would be negative. Yeah. That

Jeff Wood (31m 27s):
Makes sense. We have some folks doing research on, on location and where people were moving in and there was a longterm kind of a longitudinal study and they had to get you to get permission to use the data. I think it was Kerry and Macquarie wits and early Adkins and Prentiss Dantzler, but they had to really kind of dig in a lot, a lot of people to use that data. I have to get permission and they have to given information to get it. And so I think that’s probably a good way to go. It keeps, it, keeps them on the straight and narrow. Is there something like fantastical that you wish that, you know, you could use this data for? Is there something that you’ve dreamed up that maybe in the future could be possible? That’s kind of, you know, outside of the bounds of your usual work now, but maybe in the future it could be super fun and useful.

Jeff Wood (32m 8s):
That’s a really good question. I wasn’t ready for that. Or,

Alex Hoffman (32m 12s):
You know what I mean? I, so I do think that one of the things that, that I wish, or just when I think about, you know, doing didn’t do, or we did the analysis is a dream have a day where the different data sources actually talk to each other. And when I say that, what I mean is that, you know, if we have a primary data source, whether we’re using like origin destination data, but then like we’re also looking to figure out like, how does that affect like land use decisions or how that effect maybe like home prices in a city as an example. And I just think that one of the real challenges that I have and that I think a lot of people we deal with in this space, I have to confront a on a daily basis is like the amount of time that it takes just like clean the data on it and clean the information and get it to a point where it’s actually to a place where you can start to make, you know, different insights, the nerd in me.

Alex Hoffman (33m 3s):
And when I went to, to see, you know, like into the future, what would be great to do is I think that the challenge with the current tools that we have is that they’re typically like facing backward. Like we can look at and say like, okay, here’s something that we did. And it’s in the, have the impact that we wanted to have the implications for what we were hoping for. And so then we continue to do like this thing, or we were kind of like a, swinging a bat in the dark and hoping that we make contact. And I think it would be really great, you know, if we can get to a place where, because of the like AI and some of these different things that exist, like can these tools start to become a little bit more predictive? So that way we’re making the right decision the first time, as opposed to being like, okay, took a six tries and we use data to make this decision.

Alex Hoffman (33m 49s):
And finally, on the seventh time we got this decision made, right. I think that would be something that would be really helpful for cities. And I just think that the other thing to do that would be really great is just, again, my experience of being in like this mid-sized city at that falls out of a traditional, like a really big cities is just like, how do we make these tools scalable to like other cities? Because like, that’s one of the challenges that we’re dealing with right now with the digital divide, these communities that have the communities that have nots, and if we’re gonna move towards being, or maybe a profession that really relies on data to make those decisions, like what implications does it have for these maybe like smaller cities or cities that don’t know how to have the same type of resources.

Alex Hoffman (34m 32s):
Like we don’t want to get them a position where they get left behind. And so I just think that as we think about these tools, moving forward to the future, if they’re going to be a mainstay in the planning profession, which I think that they will be, how do we make them scalable? So that way they’re accessible it’s or these different communities. That’s a really interesting

Jeff Wood (34m 51s):
Point. You know, there’s the bill recently where somebody was writing a bill, I think it was either the or something along those lines where somebody was saying that in order to get better zoning changes in cities and especially mid-sized cities and smaller cities, we need to kind of create tools that people can use because they don’t have the, you know, the staff capability or at least a staff availability. They probably have the capability, but just everybody’s overworked because there’s so much to do in a larger city, you have more resources and you have more money and you have more access to tools. So maybe that’s part of the answer is some way a figure out how to allow the tool to be used by more people because you give them the basic capacity, I guess, is the word I’m looking for. Yeah,

Alex Hoffman (35m 27s):
Exactly. Cause I mean, I think about, you know, when I first started on my career, I mean, I was working in a city where like I was the city planner. I was the only one or, you know, and I think that there are a lot more cities that are like that than the cities that have the multi-layered, you know, department or a sections within the department that can do with these different things. And so to a point, I mean, I think that definitely a part of the conversation is like, can it be opensource? Can it be something that anybody can like look at and figure out how to use and get the same insights that the San Francisco gets in. There are a small town where, or where they live. And so I do think that that’s gotta be a part of it. I mean, it shouldn’t

Jeff Wood (36m 2s):
Take a pandemic with extra free time to

Alex Hoffman (36m 7s):
No, exactly. Right. Yeah. Or like if I had a life outside of doing planning things to exactly how it would be, it would be good. Well, Alex, where can folks find you online? Oh yeah, certainly. So it can look for me online. I have my LinkedIn profile Alex or from an a, a several password. And then I also host not a competitor podcast Geoff, but I have a, another podcast called a city planning or matters that people can listen to. And so I lose the new episodes every two weeks on Fridays, nobody’s

Jeff Wood (36m 37s):
Ever a competitor. I feel like, you know, we’re all on this together and get as much information and get out there as possible. And so I’m not worried about it. You shouldn’t be worried about it. That’s the whole goal of The Overhead Wire to share information. So let’s get it out there. Yeah, absolutely. My last question, what’s kind of the future of your work and whether some of the next steps. Yeah.

Alex Hoffman (36m 56s):
So I mean, I think that the first thing that I think that we really like to do it is just to get in the organization that I work in and the habit of using data to make decisions. Again, it may seem like it’s something that’s really obvious, but I think that that’s something that is a critical first step is can we get people to see the value in using data to make decisions? And then I think, you know, the, the, the second thing that I would really like to do, and like you were mentioning, you know, information is power is just really trying to put out as much content as possible, like within my sphere of influence, just to make people aware of things. That’s really what I strive to do when I’m creating these different visuals.

Alex Hoffman (37m 38s):
Isn’t to try to come to the conclusion for people. It’s just making the information available, because I think that there are a lot of people who are interested in these things and want to see, you know, does the city care about it does a city that I live in a look at these issues that I think about, and just being able to put out information and a way that people can look at it, it can come to the whatever conclusions is they want to do. And if at the end of the day, they’re more informed because of it, that’s something that, that I want to achieve. And a lot of the strategic initiative that’s coming from the group that I manage. That’s

Jeff Wood (38m 11s):
Awesome. Yeah. And I mean, that’s how I found out about what you’re doing because you shared it on LinkedIn and I saw, and I was like, wow, this is a really cool, so I appreciate that for sure. Well, Alex, thanks for joining us. We really, really appreciate your time. No,

Alex Hoffman (38m 22s):
Jeff, and thank you so much for having me today. It was a really, it was an honor to, to be on the show as a listener. So I really appreciate it.