Emily Ruetz: All right. Hi, everyone. I am Emily Ruetz, the Director of Technology at NSGIC. And I'm here today to talk about the National Spatial Data Infrastructure, or NSDI with Frank Winters. Frank has been in this industry for 37 years, and he spent most of that time in State government. So, to start off, Frank, 2 01:00:27,559 --> 01:00:29,599 Emily Ruetz: Why the NSDI? 3 01:00:30,039 --> 01:00:48,119 Frank Winters: Thanks, Emily. Wow, 37 years! That's hard for someone that's 35 to say. But anyways, I feel like I'm 35. Flew by. Thanks for being here! Why the NSDI, let's let's look at the big picture to start with. Many of the grand challenges facing our nation and our world 4 01:00:48,320 --> 01:00:59,719 Frank Winters: are best understood in the context of space and time, and they're best managed there as well. Spatial data is key. But building spatial data can be really expensive, and it can be really hard work. 5 01:01:00,320 --> 01:01:01,960 Frank Winters: It's absolutely necessary. 6 01:01:02,840 --> 01:01:04,400 Frank Winters: We are at this point 7 01:01:04,639 --> 01:01:10,480 Frank Winters: where the only way to keep up with the the demands of the data that we need 8 01:01:10,960 --> 01:01:18,719 Frank Winters: for these grand challenges is to work together. And by its nature, the technology we're talking about is inherently collaborative. 9 01:01:19,920 --> 01:01:25,159 Emily Ruetz: Yeah, nice. So for those listening today, what, what can they expect? 10 01:01:26,039 --> 01:01:43,239 Frank Winters: Well, what I hope to accomplish is kind of create that common baseline on the National Spatial Data Infrastructure, the NSDI. My hope is that in the next few minutes you, the listener, will really be kind of put at ease with your common understanding of the NSDI. What it is, 11 01:01:43,519 --> 01:01:47,719 Frank Winters: what it could be, what it should be, and really what your role might be. 12 01:01:49,079 --> 01:01:49,800 Emily Ruetz: Yeah. 13 01:01:50,480 --> 01:01:56,039 Emily Ruetz: Okay. So to start super super, basic. What is the NSDI? 14 01:01:56,039 --> 01:01:59,440 Frank Winters: Yeah, that's that's great. It's actually a harder question you might expect. 15 01:01:59,639 --> 01:02:16,599 Frank Winters: So feel at ease if you don't have a quick answer. But per the Geospatial Data Act. I'll refer to that as the GDA. The NSDI is the technology, the policies, the criteria, the standards, and the employees. Although I'm not really an employee. In this case, maybe we add volunteers to that. 16 01:02:16,719 --> 01:02:31,920 Frank Winters: necessary to promote geospatial data sharing throughout the Federal State, tribal, local governments, private sector, including, not for profits and higher education institutions. So it's, in that definition, it's not 17 01:02:32,039 --> 01:02:39,960 Frank Winters: the actual data. We'll get to that in a second. And the GDA, the Geospatial Data Act, is really silent on what themes are included. 18 01:02:41,199 --> 01:02:42,000 Emily Ruetz: Okay. 19 01:02:42,159 --> 01:02:50,199 Emily Ruetz: So we've got the NSDI. We've got the GDA. There's a lot of acronyms here, so can we get just a little vocabulary lesson? 20 01:02:50,719 --> 01:02:58,880 Frank Winters: Absolutely. So let's start with the NSDI to reiterate technology policies, criteria standards and employees, really 21 01:02:59,039 --> 01:03:02,519 Frank Winters: to facilitate data sharing across all those sectors. 22 01:03:03,039 --> 01:03:24,880 Frank Winters: I also look at other parts of the NSDI and say, the data sharing is not just. Here's my data. Here's your data. We make this available. It's the shared responsibility for getting that data right and letting people all play the right role. So that's what the NSDI is to me. And it's my kind of interpretation of what's in the GDA. And that's really kind of the NSGIC view on that. 23 01:03:25,559 --> 01:03:32,719 Frank Winters: So we'll move next into the Federal Geographic Data Committee, the FGDC, and that is 24 01:03:33,159 --> 01:03:42,920 Frank Winters: representatives from 32 agencies on a committee that really shape the federal contributions and the federal responsibilities on the NSDI. 25 01:03:43,320 --> 01:03:49,719 Frank Winters: Okay, so we go from the NSDI, which is national, not federal, to the FGDC, focused on Federal. 26 01:03:50,199 --> 01:03:56,400 Frank Winters: Now, how does that work? It works because there's representation from all the sectors that feeds 27 01:03:56,519 --> 01:04:05,239 Frank Winters: the FGDC. So the National Geospatial Advisory Council, the NGAC, currently has 14 members, and that is 28 01:04:05,400 --> 01:04:10,559 Frank Winters: representatives from all the sectors, from private sector to federal, to state, 29 01:04:11,000 --> 01:04:20,360 Frank Winters: local governments, and they have the role of advising FGDC on policies, priorities and how to really get their work done. 30 01:04:20,840 --> 01:04:33,639 Frank Winters: So NSDI, FGDC. NGAC feeding the advice to FGDC. And now let's talk about the actual data sets. So those data sets. There's a little bit of acronym here. 31 01:04:33,880 --> 01:04:42,039 Frank Winters: The term national geospatial data asset is really a category of geospatial data. 32 01:04:42,679 --> 01:04:49,280 Frank Winters: and that data is needed by many. So, for instance, imagery might be one of those, is one of those categories. 33 01:04:50,039 --> 01:04:53,199 Frank Winters: The actual data sets, the national 34 01:04:53,519 --> 01:05:19,480 Frank Winters: geospatial data asset data sets, are the individual data sets that you can work on. You just can't work on imagery. You can work on a particular type of imagery to certain specs, and there's different types that come together to satisfy the needs of the theme. So the theme is the asset. The data sets are the pieces that we can actually put together to create that theme. So, a little bit of vocabulary; hopefully that clears it up and gives us a little 35 01:05:19,480 --> 01:05:30,880 Frank Winters: explanation. You can really think of the NSDI as an umbrella that encompasses all this. And most people do think of data when they think of the NSDI. But really, more importantly. 36 01:05:31,000 --> 01:05:40,519 Frank Winters: It's the collaborative infrastructure. The people, the policies, and the ways of conducting work, so we can all 37 01:05:40,920 --> 01:05:43,519 Frank Winters: build it once and use it many times. 38 01:05:44,119 --> 01:05:44,960 Emily Ruetz: Okay. 39 01:05:45,599 --> 01:05:54,719 Emily Ruetz: Nice. Thank you for that. I appreciate it. Just a quick question. Like, while you were talking about that you mentioned that 40 01:05:54,840 --> 01:06:05,360 Emily Ruetz: NGAC, that that's like a multi-sector committee or council. And so how like? How do you find out who represents your sector? 41 01:06:05,960 --> 01:06:28,599 Frank Winters: Yeah, sure, that's a great question. And they're just published around the web. So if you use your favorite web search engine and and just look for NGAC Member list, you'll find a list of those members. There's not necessarily contact information there, but at least you know who they are and what organization they belong to or employed by. And then, really the best bet would be to reach back out to NSGIC. 42 01:06:29,000 --> 01:06:35,880 Frank Winters: All of those members have some representation in NSGIC. So we'll help you either find the contact on the committee, 43 01:06:36,000 --> 01:06:54,440 Frank Winters: or we can help you at least have NSGIC's more, maybe more reachable, more available contact into that organization so you can feed up through them. But it really is a way that your voice can be heard, and NSGIC is happy to help funnel that. That's probably part of why we're here. 44 01:06:55,480 --> 01:06:59,599 Emily Ruetz: Well, thank you. That was a very nice way of saying "Let me Google that for you." 45 01:07:00,239 --> 01:07:01,320 Frank Winters: Yeah, right? 46 01:07:01,320 --> 01:07:07,440 Emily Ruetz: Yeah. Okay. So going back to the GDA. So what, what's at the core of that? 47 01:07:08,199 --> 01:07:34,000 Frank Winters: Yeah, there's one paragraph in the GDA that I will read to you here that to me is the is the core of the intent of the GDA. The GDA requires a lot of other stuff, but each covered agency shall coordinate and work in partnership with other Federal agencies, agencies of the state, tribal and local government, institutions of higher education, and private sector, to effectively 48 01:07:34,119 --> 01:07:37,000 Frank Winters: sorry, to efficiently and cost-effectively 49 01:07:37,199 --> 01:07:43,960 Frank Winters: collect integrate, maintain, disseminate, and preserve geospatial data built upon 50 01:07:45,079 --> 01:07:50,719 Frank Winters: existing non-federal geospatial data to the extent possible. 51 01:07:50,880 --> 01:08:04,880 Frank Winters: So they're telling the Federal agencies and the the GDA can only really dictate what the covered agencies, those that are in the purview, and this law applies to. That they must collaborate and coordinate, and they must 52 01:08:05,000 --> 01:08:19,800 Frank Winters: look externally first, to the extent possible, for sources of that data. So it's that's pretty great. That is a mandate to collaborate. And we're on board. It's not burdensome to the States in that, that's what we're hungry for. 53 01:08:23,039 --> 01:08:32,039 Frank Winters: So there's there's a little bit of of a difference, and there could be difference of of an opinion 54 01:08:32,239 --> 01:08:45,840 Frank Winters: on how different organizations view the GDA. And is it mainly a Federal reporting requirement and a responsibility? Or is it mainly a blueprint 55 01:08:46,640 --> 01:09:08,439 Frank Winters: and a mandate to collaborate? I'm hoping it's a little bit of both, but I'm hoping we emphasize the the actual intent of the GDA is to really foster the collaboration. The reporting requirements, in NSGIC's opinion, are to have some accountability and and to measure our success on the collaboration that we do, and the efficiency that we gain. 56 01:09:09,439 --> 01:09:15,439 Emily Ruetz: So with all of that in mind, can you highlight a couple of examples of where this is working really well? 57 01:09:16,319 --> 01:09:20,640 Frank Winters: Yeah, that's a great way to to look at this. It's so much easier to to 58 01:09:20,760 --> 01:09:35,520 Frank Winters: kind of take a pattern that's working and apply it to to other patterns than it is to rail against those things that we're frustrated with. And there is a level of frustration that is, you know it's it's appropriate. We all care right? So the first one is the National Address Database. 59 01:09:35,880 --> 01:09:41,680 Frank Winters: Addresses are assigned by local government. Those are rolled up typically by States. 60 01:09:42,399 --> 01:09:43,840 Frank Winters: FGDC 61 01:09:44,119 --> 01:10:02,039 Frank Winters: had an enlightened moment, and FGDC voted to use the NENA standard as the basis for the National Address Database, and that was not something everyone agreed upon. But they really took the input from other stakeholders outside of the Federal Government 62 01:10:02,560 --> 01:10:04,359 Frank Winters: that made it clear that 63 01:10:04,720 --> 01:10:31,479 Frank Winters: the use case of 911 is what caused most of the energy around getting that data right. The local governments really really care that 911 calls are answered properly, and the location of the address is paramount. So the NENA standard being connected with 911, the vote fell towards using the NENA standard. So that is a really good example of that collaboration and considering lots of lots of perspectives. 64 01:10:32,359 --> 01:10:34,119 Frank Winters: Private companies then 65 01:10:34,840 --> 01:11:01,039 Frank Winters: have an interest in using that data. But then, again, so do we. People of our nation are not coming to government websites to download millions of address points. They're experiencing our work using their phones or ordering their pizza or their package delivery. And that's the day-to-day transactions that they care to get the data right on, and then a side effect from that is that when the 911 call happens, 66 01:11:01,520 --> 01:11:03,439 Frank Winters: that data has been exercised. 67 01:11:03,720 --> 01:11:13,279 Frank Winters: And that's a a great kind of a view of the whole supply chain. But there's one interesting missing piece that I didn't cover there, and that is 68 01:11:13,840 --> 01:11:19,960 Frank Winters: postal service has an interest in getting addresses right, but the addresses are signed by local government. Postal service 69 01:11:20,319 --> 01:11:22,560 Frank Winters: has the value, add, of 70 01:11:23,199 --> 01:11:31,840 Frank Winters: defining the ZIP codes. That is clearly their purview. So the postal service right now is working with the folks at USDOT 71 01:11:31,960 --> 01:11:48,119 Frank Winters: to, on a plan to have them get all of the ZIP codes correct on the actual address points. So that's, just think about that whole data supply chain. We've got local governments, state governments. We got not for profits with the standards. We've got 72 01:11:48,239 --> 01:12:14,520 Frank Winters: all different use cases. We've got private sector, Federal agencies supporting it. Another Federal agency, adding, adding their value add to the data, and it goes to our citizens just as part of their normal course of action. And in doing so, if we can make transportation and delivery and people driving around some tiny percent more efficient? Think of the the impact the societal impact that we've made. So that is 73 01:12:14,600 --> 01:12:37,800 Frank Winters: a model to to reproduce. And you know, it's like I said, it wasn't all easy to do, but here we are. We're collaborating. It's not complete. So we've got some work to do to close the gaps and make that a comprehensive nationwide data set. But it's great where it's it's way, more than half the the nation that data is really ticking along great. 74 01:12:38,720 --> 01:12:54,560 Frank Winters: So I think about the terms of data supply chain. Where is the upstream? Who touches it? How often and and when and how does it go back? And how does it maximize its impact? That data supply chain is different for every data theme. Let's look at 75 01:12:54,760 --> 01:13:06,079 Frank Winters: the US Census and the and the decennial census as as another example, a radically different data supply chain. The Census Bureau has both the mandate and some funding to collect the census. 76 01:13:07,000 --> 01:13:10,279 Frank Winters: so it's clearly theirs to start with. It's a Federal data set. 77 01:13:10,720 --> 01:13:21,359 Frank Winters: Yet the census needs addresses, boundaries, roads. They need the right hydro to make all that geography work, and they need that stuff all updated because all of that is dynamic data, as we know. 78 01:13:22,479 --> 01:13:32,119 Frank Winters: So they reach out to local governments for addresses and roads, for instance. Maybe they reach out to other Federal partners to make sure they have the right hydro to make census geography 79 01:13:32,720 --> 01:13:37,079 Frank Winters: and all that comes together, and they've got big processes to do that nationwide. 80 01:13:37,199 --> 01:13:43,279 Frank Winters: Now, in a cool example, is that in New York, there's an MOU with the Census between the State of New York and the Census, 81 01:13:43,720 --> 01:13:54,920 Frank Winters: and it really instructs that instead of running their normal process for outreach to local governments, the Census Bureau works directly with the State GIS office and 82 01:13:55,359 --> 01:14:14,079 Frank Winters: adds another use case to the collaboration that is already happening and the coordination that's already happening. Instead of going out to local governments with a duplicative ask, and local governments are saying, we already gave this to the State. Why are the Feds asking? Well, that doesn't happen in New York? Because the Census Bureau was receptive and 83 01:14:14,239 --> 01:14:23,840 Frank Winters: understood the the needs and and how this impacts local governments and worked with us. So you know another great example of just that collaborative 84 01:14:24,000 --> 01:14:35,680 Frank Winters: non bureaucratic, but managing something different in a state where where you have something different to to contribute. So couple of really good examples. But you think about those data supply chains. They're very different. 85 01:14:37,800 --> 01:14:39,359 Emily Ruetz: Yeah, definitely. 86 01:14:40,479 --> 01:14:45,239 Emily Ruetz: So then why do you think there are different ideas on, like what the NSDI is? 87 01:14:46,000 --> 01:14:58,159 Frank Winters: Yeah, that's great. And I don't think that there's necessarily that's that's always a problem, because there's room for differences in perspective on the NSDI. For a couple of reasons. One is 88 01:14:58,359 --> 01:15:00,800 Frank Winters: that many of us play different roles. 89 01:15:00,920 --> 01:15:23,840 Frank Winters: you know, an address authority in local government has a whole different lens than the 911 coordinator who's trying to dispatch calls, from the person in the not for profit, you know, working on the standards, from the Federal person that's rolling it up, from the private company that's trying to satisfy their customers. Those are very different roles. So we all kind of look at it differently. 90 01:15:24,199 --> 01:15:30,319 Frank Winters: And theme by theme, there's different data supply chains. So there's differences in all of that. 91 01:15:30,439 --> 01:15:33,359 Frank Winters: But if we boil it down to 92 01:15:33,640 --> 01:15:40,479 Frank Winters: let's have the courage to go back to the intent of the GDA. And that is to make sure we collaborate 93 01:15:40,840 --> 01:15:50,479 Frank Winters: and build it once, and use it many times. So at the core, we can all look at it the same. But the way we actually conduct our work every day is appropriately different, because we have different roles. 94 01:15:51,399 --> 01:15:56,439 Emily Ruetz: Okay, cool. So then, with those different perspectives and 95 01:15:56,560 --> 01:16:05,319 Emily Ruetz: and everything like, is there an end goal in mind or like, how do we know when we're done with the NSDI? 96 01:16:05,760 --> 01:16:29,199 Frank Winters: Yeah, that's that's the question that I think frustrates a lot of people. Like, we've been at this so long, how come we're not done? And sure, there could be some more collaboration that have happened over the years. But right now I think that the climate is really right for a big, a big change in how we're seeing that. We're just seeing great collaboration across lots of sectors and a hunger for it. But I hope we're never done. 97 01:16:30,079 --> 01:16:37,600 Frank Winters: I hope that the use cases continue to grow in reality and and more 98 01:16:37,960 --> 01:16:51,199 Frank Winters: more intense use of higher and higher resolution to solve more and more realistic problems, and and just continues to grow in a way that we have a higher demand for higher, spatial and temporal resolution. 99 01:16:51,960 --> 01:17:05,640 Frank Winters: Now, will we ever be finished? Because I think the demand will continue to outstrip our ability to meet that need with the spatial data. And that's fine. We'll be playing catch up and we'll leave a red trail of goodness along the way. 100 01:17:05,880 --> 01:17:35,359 Frank Winters: But I think in a few years, we're going to look back at this point in time and say, "Oh, wow, we really are collaborating!" Folks are really, they really are considering the inputs, the needs and and the impact that their decisions have on other people in the data supply chain, and they really will start thinking about this as a healthy ecosystem. I think that's already changing. So in one regard, we"ll never be done. In the other regard, I think we're going to just sort of take for granted and have to deliberately look to say, yeah, we've come a long way in our ability and and 101 01:17:35,600 --> 01:17:48,680 Frank Winters: and our need to collaborate. And by working in that way, we're not talking about putting more hours in at the office. We're just talking about looking at it differently and and feeling good about our contribution, because it's part of something way bigger than ourselves or just our agency. 102 01:17:49,760 --> 01:17:50,359 Emily Ruetz: Yeah. 103 01:17:50,640 --> 01:17:53,520 Emily Ruetz: Much more complex than "Tick, done!" 104 01:17:53,680 --> 01:17:54,199 Emily Ruetz: Got it? 105 01:17:54,199 --> 01:17:54,960 Frank Winters: Absolutely. 106 01:17:55,079 --> 01:18:01,720 Emily Ruetz: Cool. Well, thank you. This has been really fun. Do you have any closing thoughts? Anything you'd like to share? 107 01:18:02,640 --> 01:18:20,880 Frank Winters: Yeah, going back in my career and my role, I used to think of contributing to national data sets as altruistic. We really had everything we thought we needed in order to get the work done of the State agencies we were working for. But now I've realized that that collaboration, 108 01:18:21,760 --> 01:18:27,920 Frank Winters: considering the needs and perspectives of players all along the data supply chain 109 01:18:28,079 --> 01:18:32,359 Frank Winters: is the only way to efficiently manage the data we need. 110 01:18:32,680 --> 01:18:47,520 Frank Winters: And the only way to also maximize the positive impact on our work on the people of of this nation and the people of our constituents, our our people, and the and the customers of the private companies we partner with are the same people. 111 01:18:48,560 --> 01:19:06,880 Frank Winters: So closing thought there is also look forward to more NSGIC podcast in a series bringing in different perspectives on the NSDI, because we're going to kind of model the behavior and have people in that look at it differently, and we'll talk through it, and together we'll we'll advance this. 112 01:19:07,880 --> 01:19:13,439 Emily Ruetz: Awesome. Thank you so much for for joining me today. Really appreciate it. 113 01:19:13,920 --> 01:19:15,800 Frank Winters: Yeah, thank you. Emily, this was really fun.