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Christian Martinez, "NYC Open Data Student Gallery" (Brooklyn College CUNY, 2026)
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- [00:01]
Christian Martinez
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Gregory McNiff
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Christian Martinez
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- [01:24]
Gregory McNiff
welcome to the New Books Network welcome to the New Books Network. I'm your host Gregory McNiff and I'm thrilled to be joined by Christian Martinez, the author of NYC Open Data, Student Research Projects and Reproducible Psychological Research. The publisher is Brooklyn College CUNY and it was published in early 2026. Christian is a faculty member of the Ms. Program in Psychological Research at Brooklyn College cuny, where he teaches graduate level stats and research methods. In the fall of 2025, he designed and taught the inaugural offering of Reproducible Psychological Research, a course in which students use R R markdown and publicly available New York City data to conduct original research. He also developed the open Access companion textbook Reproducible Research in R and the NYC Open Data package, which supports access to an analysis of New York City open data. This is all available for free and I'll provide the link at the end of this interview. I selected this interview because it really brings together not only data and open data and discusses data and literacy, but it has a direct impact on New York City in an unusually accessible way. The book follows students through the full research process, from clearing the imperfect data sets to analyzing results and explaining their limitations. The Case studies cover a wide range of topics from housing and public health to juvenile incarceration, climate risk, and even the Madison Square Garden effect. So it'll appeal both to technical and general audiences. And it really is just a very insightful reading from some very gifted students. Christian, thank you so much for joining me today to discuss your work.
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Christian Martinez
I should say, Gregory, thanks so much for having me. It's an honor.
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Gregory McNiff
Great. Kristen, just to level set for our audience, can you tell us a little bit about the Ms. Program and psychological research at Brooklyn College and what type of student would enroll in it?
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Christian Martinez
Absolutely. So this is a brand new program inside the psych department at Brooklyn College cuny. With that being said, because it's a brand new program, there are goals and we've been instructed, which is rightfully so, to be as malleable as possible. The goal is for this to kind of be an in between for students that are potentially considering maybe going into a PhD program, a little bit more into research. But it also has kind of shifted, especially after the class and seeing what my students were interested in. And maybe we want to divulge a little bit more into what more of the public sector looks like or outside of just academia, because I think overall, inside, one of the best things about being a student and learning and going to school is you get introduced to new things hopefully and you're like, wait, I didn't even think about that. So it ranged from students that were in California that blew in to take this program. Some were people that lived in Staten Island, Some people got their undergraduate degrees at Brooklyn College. So really a wide variety of students. And even if you look not only at their research projects, we did in this program in the class, excuse me, but their thesises, I mean, they had totally different interests. And it was so cool to see that this program brought so many different people, which I think is actually one of the best parts about graduate programs overall. So some are going to PhD programs now, some are looking for jobs. And we really got a diverse cohort in just nine students.
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Gregory McNiff
Wow. Yeah. No, I mean that diversity came through in the case studies, their final projects. And I get into that. I just want to ask a follow up. You know, we all familiar with the traditional statistics programs. What's unique about this? You know, I'm calling it the Reproducible Psychological Research Program. Why did you, I think you helped found it at Brooklyn College. If somebody said, well, aren't there stats programs at, you know, every other college? What's unique about this one?
- [05:37]
Christian Martinez
Great question. So I want to say that this is the first time I've ever taught at the graduate level. I'd previously been teaching at John Jay College of Criminal justice in their psychology program, but more in, like, more traditional psychological classes. This cohort comes, this program comes at Brooklyn College, and they're looking for someone to really be the person that says, hey, you have your data, you've done your experiment. What do we do now? We have so many classes that teach you how to conduct research, how to do statistics, but never the. The bridge between them. And that's what the real goal of this course, reproducible Psychological Research was. And shout out to Brooklyn College because they gave me, they, they obviously sent syllabi of similar courses and things that they've taught before, but they really gave me free range to design the course how I saw fit, which I think is fantastic in terms of academia because every person that teaches has specialties. Now, for me, when I'm coming into this course, this, there are two things I'm looking at. One is that these are graduate students, so we can treat them differently than undergraduates because sure, we had different ages, age ranges, demographics, et cetera, but they're all graduate students and that comes with a different level of seniority and treatment. But I really wanted to take what I think is one of the biggest problems in academia currently, which I've experienced with my undergraduate students, which was creativity. When I have taught at the undergraduate level, it's been flooded with, I need specific directions. Hey, we're going to create a research project or we're going to do this. Pick your topic. Well, I don't know what I should do. I've always been given the instructions. And anytime I've had the executive functioning opportunities or creative opportunities to think on my own, I freeze. So in my mind, whether you're going into a PhD program or going to work for a nonprofit or a startup or whatever, the most important thing is to be able to think on your own and be creative because you ultimately are what's setting you apart from everyone else. So that was my main goal in how do we treat people at the graduate level to be as creative as possible, no matter which way you went. So that was my real backing for the class.
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Gregory McNiff
Oh, that's interesting. And presumably that means giving them unique tools. And I want to talk about R programming language and R Markdown. But Christian, do these students come in with any type of statistical or economic or policy background? Who is the course of the program open to? I mean, it seems like a very diverse group.
- [08:33]
Christian Martinez
Yeah, so to give you an example, one of my students was, I think they got their undergraduate degree in English and they wanted to transition more into psych, which I actually think English is a great undergraduate degree to have overall, and it's very transferable. Some students, obviously English, some got their undergraduates in psych before they took my class. So I was in the second year of the cohort, so they had one full year before me. I was starting the fall semester of their second and they at least took a statistics course that was a little more in, hey, what's the actual foundation behind anova? That was a correlation actually run. And they were doing some of these techniques by hand, which I think is incredibly invaluable and something that's overlooked because sure, you're taught anova, but what is even an anova, you could just see the results, but the engineering behind it, which is crucial and made up hundreds of years of scientific literature and everything that we have now. So I was also the bridge not only between taking data and making it effective, but making sure that the underlying principles behind the statistics, the statistics were, were used.
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Gregory McNiff
Yeah, that, that makes total sense. Okay, could you talk to me a little bit? I think we're all familiar with programming languages. Python, for example. What is unique about R to this program?
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Christian Martinez
Yeah, that's a great question and a question I actually get all the time now. R, it seems like, is very popular inside academia. It's actually the first place that I was introduced to it when I was coincidentally getting my master's. And what I think really helps stand out is Python's fantastic for being a ubiquitous language. You could kind of do everything. And I think that more and more languages are actually getting to that point. But another topic, R is fantastic for undergoing statistics and in my opinion, maybe even more important, visualizing those statistics, because it's something that I tell my students all the time. Hey, number one thing you do, you load the data, you make sure it's clean and visualize it first. Make sure you can see it, understand it, because the visualizations can tell you a story that the data may never be able to tell you just in pure numbers. So I think that's what R does best. It they would easily facilitate statistics and then also easily visualize those. And you could see in any of my students projects, I made sure I hammered in, Visualize, visualize, visualize. And it's something that they did because I always ask the question and I tell them like, hey, like, what if you were showing this to your grandma? And at least Mine, she doesn't know numbers at all. She is not a math person, and statistics is definitely not in her wheelhouse. But she can understand a pie chart, she can understand a bar graph. She could see, all right, this is up, this is down. She can understand lines. And visualization is incredibly important because not only for yourself, but for your audience, which is ultimately what you're going for.
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Gregory McNiff
Yep. You almost anticipated my question going through. I kept thinking, how much is the visual element? Because like your grandma, you know, I saw the programming language and I'm not a programmer, so it didn't mean much to me. And I read the conclusions, but I saw the charts and intuitively I was like, okay, I, I understand A, the correlation or B, the anomalies, you know, what you're pulling out. So. And specifically, does this R programming language have a visual element to it, or is that something you, your students just emphasize separately?
- [12:15]
Christian Martinez
I would say both. I'd say that in an ideal situation, no statistics or to some degree, mathematics classes should ever not talk about visualizations. Now, R comes like when you download R, if you were downloading R for the first time, installing it on your computer, they have base ways to do visualizations. But one of the best things about R Python and anything like this is that it's open source, so people can see a problem and say, I want to fix that myself, or I have this amazing idea, let me see what I can do with that. And so, thankfully, and shout out to the developers, there's a package called ggplot2 which makes it incredibly easy, and not only easy, but malleable to create visualizations. And I would say, I think with somewhat, some high certainty, 100% of all the visualizations made were using ggplot or an extension of that somehow.
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Gregory McNiff
That's fascinating. I want to get into the requirements or get into the final projects. Could you talk to us about why, what the requirements or what the, you know, the thesis or the instructions were for your students?
- [13:32]
Christian Martinez
Yeah. So I think somewhat, being from New York City, I always have a trick up my sleeve and I always got this plan. So imagine this is the inaugural cohort. This is my first time teaching in the program. This is a brand new class, I'm a brand new faculty member. So there's a lot of opportunity in my mind now coming into this. Obviously all of my students are working on a thesis and they're working in labs, but I don't come with that same opportunity in a traditional sense. I don't have a lab that you can come and work in, and we're not working on specific research and this and that. But I had an idea. What if we can circumvent that and do research that could be done outside of a traditional lab and get exposed to the greater population? So every year, New York City hosts New York City Open Data Week, which is a huge conference that focuses on all things related to data and New York City. And the caveat is, is that it has to be about New York City and it has to be open data. My thought was, what if we can build potential research projects that could be submitted and potentially presented at New York City Open Data Week. This way, even though I'm not a traditional trajectoried faculty member, we can still execute at the same level and get my students to potentially present at a conference, get their name out there, see what's out there, network, et cetera. And that was the backing of my research project, of our research project. Excuse me. So I went to all my students and I said, hey, we're going to conduct research. We're going to use R. And here are the two requirements. Number one, it has to be using open data. And number two, it has to be about New York City. The same exact requirements to submit something for New York City Open Data Week. Now, this is the part where almost all of my students froze as I was talking about, similar to the undergraduates, and they're like, well, what questions should we do? And I'm like, whatever you want. And it's almost like a decision paralysis. Like when you're at the grocery store and you see 400 different cereals and like, oh, my God, what should I get by which one? Are all batteries the same?
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Gregory McNiff
That's like asking if all soccer players are the same. Take Messi, the most decorated player ever.
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Christian Martinez
Is there any other player who has achieved that? No, just him. Now take Duracell. Is there any other battery with power boost ingredients inside?
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Gregory McNiff
No, just Duracell.
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Christian Martinez
Remember, goats only trust goats because they're built different.
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Gregory McNiff
And Messi only trusts Duracell.
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Christian Martinez
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- [17:11]
Gregory McNiff
Yeah, I mean, I'll let you finish, but I definitely have a follow up there. I mean, you, you sort of put them, they're graduates, so you, they jumped right in. But I don't want to pick on another city, but I will. You know, we're not doing Nashville Open Data Week or even Chicago. I mean, you a. For our audience. Could you talk a little bit more about exactly what open data is? I think people realize it's access to an enormous amount of relatively real time information. Maybe, maybe not. And then. Yeah, I mean, New York is like letting your students drink right from the fire hose.
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Christian Martinez
Yeah. And that's a perfect segue, actually, because we could talk about how we got into open data, but this was all a master scheme, Right. I wasn't just throwing mirrors to the open data out like we had planned it. So open data is data made for the public where, especially in New York City, anyone at any time has the opportunity to use it. However, which way, if you just want to go on a website and see it, if you want to pull it through an API, if you want to download it from an Excel, however you want to use it, it's open to the public. Now, New York City has a storied history about how data about New York City became open. We can go back to the 1800s where you had a very corrupt mayor, Boss Tweed, and people were somewhat. Rioting is a strong word, but they were very upset, like, hey, like we want to make information more open, more accessible. We get to 2012 and New York City in particular signs the open Data law, which basically states that any information about New York City that we can make open, we're making open mind. You, like any sensitive information, they're not putting Social Security numbers or anything like that. That's something that they're not putting. But anything they can, they're putting. So you, me, any of the listeners today can go on New York City Open Data, the data portal, and look at any data. And I mean, there are like trillions of different data points, right? Because I think there's at minimum 2,000 data sets. And for instance, the 311 data set just from 2020 to now has, I don't know, 30 million different rows of data. However many columns there are in that one. So we're talking an innumerable amount of data, really. And it's so cool because there are more storied ones that come out maybe twice a year or quarterly. But then there's the 311 data set, which I just mentioned, where it's updated every day. So as of today, June 16, I can see all of the data, all of the 311 complaints as of June 15, yesterday. So it's constantly getting updated. And it's such a. It really is. It's such an amazing feat done by New York City in that they're trying to say, hey, this is our city. We want to make sure that everything is available. And however you want to use it, use it. And what's really cool is that let's say you're looking for specific data about New York. It's not on the portal currently. You can actually send them an email and say, hey, I'm looking for so and so data. Is it on the data? Is it on the portal? And if it's not, but it's available and it's safe to put up, they'll make a new data set just for this question. So they're very. They're making it very hospitable and easy now to go with that. In R, we have data sets that come with R. For instance, there's one about cars called Empty Cars. And when I started teaching the class, I was using these base data sets, and I found out very quickly that my students did not care whatsoever. I was putting my students to sleep. Imagine I'm trying to teach R statistics, reproducible research. All the things that I was trying to teach with boring and almost like cadaver data sets that weren't alive. And so I was like, you know what? Let me switch it up. Let's start using New York City open data in our weekly classes. And that, for me, totally changed the game. And we could talk about it if you want. But that's what also inspired me to create the New York City Open Data package.
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Gregory McNiff
Okay, perfect. What is reproducible research here?
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Christian Martinez
Probably the most important question, and really the axiom on top of creativity. But the axiom I wanted to build it upon my class was reproducible means that if I run this exact same study in the exact same way, using the exact same methods, I should get the exact same results. Results. And that is incredibly important in the scientific community because in order for something to be considered more factual, more concrete, you absolutely need it to be reproducible. And again, my students are working on their thesis. I want to make sure that their thesis. Things that are real scientific experiments, papers that are going to be published, they need to be reproducible. Because if you are reading that paper, you're like, hey, like, is that true? Is that really what they found? That you can use an identical copy and get an identical result? Something that, Especially in psych research, probably because of maybe a lot of reasons, but especially in psych research, it's not always there.
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Gregory McNiff
Yeah, yeah, no, you constantly hear about, you know. Exactly. Papers, you know, when the peers that have been peer reviewed or attempt to replicate the results can't quite get there. So that makes total sense. Why is it psychological research? And I know the case studies do talk about quality of life and mental health. Is that an important aspect? Or. It almost seems like this could be urban research or public policy research. Is there any unique psychological aspect here?
- [23:22]
Christian Martinez
You know, I'm gonna go with. I'm gonna say something that might be a hot take. So. And anyone can disagree. My hot take is that psych as a sphere has grown tremendously and has now branched out and is like kind of one of the circles or like a overlapping circle in everybody's Venn diagram. And that has maybe diluted the. The participants. What I mean by that is like a lot of psych research lacks participants. Right. So, oh, I did something, but there was only five people. Or, oh, you're working with infants. Oh, but infants. How do we know that this is true? Or, oh, you're working in this sphere. But to your point, isn't this more of a social or economic or something like that? So I think a catch 22 of because it's gotten so big and it's branched out, that it as a core has maybe been a little diluted.
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Gregory McNiff
Oh, that's interesting. Okay, let's dive into some of these cases. And I think the book has nine of your students thesis the inaugural. I want to talk about each of them briefly here. The first one compares the leading causes of death with the indoor environmental complaints, particularly around mold, air quality and asbestos. Could you talk about the data set that your student looked at there and the conclusions that he or she came to?
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Christian Martinez
Yeah, so I actually had two students look at leading causes like death and indoor environmental complaints. And I think it's so New York City because, mind you, there are so many apartments and there's so many people and there's so many landlords and there's so many things to do. And I think that sometimes structural qualities can just become about so the leading Causes of death in indoor environmental complaints, especially with mold, are trying to see, like, hey, what's going on in New York? Are there a lot. And I think that these studies are great because they do find relationships between environmental complaints. And so there's two with mold, there's the causes of death and indoor environmental complaints, which I think is chapter one. And I think chapter six is mold and domestic violence reports.
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Gregory McNiff
That. Yeah, that one was. I hate to say that, almost as a standout to me. It was so counterintuitive. Yeah. And I mean, they all were wonderful. But I read that one and thought, wow, I never would have made the connection. I don't want to. I don't want to jump through chapter one. But when we talk about chapter six, that was just fascinating the way two seemingly disparate sets of data came together to provide a fascinating conclusion or analysis. But please continue.
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Christian Martinez
Yeah, yeah. So first, on the same topic, I mean, I love. One of my favorite parts of the book overall is that chapters one through nine are like, they're similar, but they're different in. It's so cool because when I gave my students the creative power and I'm like, hey, I'm not actually going to help you pick a topic like, you are a graduate student. Whatever you want to do. I don't care if you want to talk about the Knicks. I don't want to care if you want to talk about the next. I don't care if you want to talk about mold. I don't want to care if you talk about potholes. Like, whatever you want to do. And so we get these amazing and eclectic chapters. So we get leading cause of death and indoor environmental complaints. And a lot of the differences for the first one are explained by indoor environmental complaints. And that makes sense, right, because you could understand that if you're living in poor or worse conditions, your health would suffer. I'd also estimate that there's some epigenetic value because your environment can change your DNA. And so if you are living in an environment, maybe not like a direct, like, oh, I'm developing a cough because of mold and dying from the cough. But now epigenetically, I'm less healthy. And that could stem in a different way. And I think with this one, there are, like, of course, confounding variables, and we would love to maybe incorporate like poverty levels or maybe educational levels or number of people in household, which is, of course, something interesting. But I thought the first one is. Is really cool. I actually think that. Not to jump, but I think the Second one is also awesome, if I may. So the second one talks about social infrastructure and well being and the second one really talks about, hey, like, we can kind of measure poverty and we want to see if the number of events in a city is related to the poverty levels. Now the problem is, is that in an ideal situation, my student Jonah didn't have all the data that they wanted because a lot of it doesn't exist and like snapdata is not readily available. And this is a great way to talk about, hey, even though we have a robust amount of data on open data, we don't have everything. And sometimes that's good and sometimes that's bad. But for this question, I thought it was so ingenious that they used permitted events because that kind of makes sense if you're thinking, all right, like, how happy am I as a measurement? Maybe the amount of community that I have around me can measure that. And so they didn't quite find anything drastic. I think one cool thing that they are potentially looking into is also the amount of parks that are around trying to see if maybe parks or greenery and permanent events together may give a little more of an impact for that.
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Gregory McNiff
Yeah, that was interesting on the. And SNAP stands for Supplemental Nutrition Assistance Program. And to see if there was some correlation there, I guess, with how engaged the community was in terms of gathering street fairs, something like that. So that was another one that was really unique way of looking at it, the restaurants and museums that felt like that was probably one of the more commercial ones, I would say. But can you talk a little bit about the data there and maybe what your students concluded?
- [29:54]
Christian Martinez
Yeah. So first I want to say shout out to my student Joyce, because they were particularly stumped by what they should do. And so I just asked the question, I was like, all right, Joyce, like, what do you like? She goes, what do you mean? I said, what do you like? Just tell me what you like to do. Hobbies. Well, she's like, well, I love to go out to eat and explore new restaurants. I said, all right, cool. What else do you like to do? She goes, actually, I love to spend time at museums. I said, all right, what about creating a way, like a research question about those? And she goes, I can do that. I was like, you can do whatever you want. And so once we got the. And we talked about more ideas on again, just what they like as a person, because open data is fantastic because if you have a question you can answer yourself, you don't have to go to someone else. And so they, first of all, I mean, an unbelievable amount of data cleaning with this. This was blood, sweat and tears and shout out to Joyce because it really took a lot elbow grease with this. And I think what was so cool is that she was able to merge. Excuse me. Three different data sets. One being New York City restaurant locations, two being museum locations, and three being the ratings of the restaurants. And they wanted to. Excuse me. I really think the standout part of this is we're talking about visualizations, but she created an interactive map that's color coded that contains all of the museums and restaurants in New York City with all the data that was available there. And you're now able to see, okay, I went to the Museum of Natural History. I'm hungry. What restaurants should I go to around here? So it really became. It can be. Because there's even more to do, but it kind of could be like the originators of the Michelin stars, where they were trying to make it where you could go wherever you were going. I know it's Michelin tires, but wherever you were going, look at different restaurants. And so this is so cool because her, like, this is so useful for her and so many times you're building things for other people, but this is something that she uses. All right, I'm going to this restaurant. Maybe I want to go museum afterwards. So I, I think that besides the, the conclusions and a lot of the things weren't statistically significant, but it's from a visualization standpoint and mapping standpoint. Amazing.
- [32:23]
Gregory McNiff
Yeah, the map is really impressive. And Kristen, just to clarify, the rating data, did that come from Open City or. I think she referenced a site on Kaggle or Kaggle.
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Christian Martinez
Yeah, that was the. It's still open data, but it's not from the New York City open data portal.
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Gregory McNiff
Got it. And you can combine the two, obviously ratings or user reviews or. Exactly. I mean, and I realize open data is such a big part of this, but just wanted to clarify that speaking of what you love and maybe combining open data with other data, the next one, the Madison Square Garden effect, probably is the most timely interview I've ever done given as the city. I'm guessing your student here is if he. He or she wasn't. Is now a nick span. But could you. Could you briefly talk about that? What type of data and the conclusion there.
- [33:18]
Christian Martinez
Yeah, this one was particularly fun because it was kind of against the grain. All of my students, excuse me, eight of my nine students use data from New York City open data, except for this one, but it's an amazing feat because it goes to show you that it doesn't have to be from New York City open data to both be about New York City and be about open data. So I thought it was so creative. And this student is from California and they love the Golden State Warriors. And yeah, I mean now you ask them, they're probably going to say that they're a Knicks fan like everyone else. But they were, they were like, hey, is it cool if I do something a little more basketball related and a little more my personal interests related? And again, I'm thinking creativity transferable things. So of course you could do that. And so this student wanted to see. All right, like everyone talks about New York City and Madison Square Garden MSG being the mecca of basketball, the Mecca arena. Does it actually have an effect? And they really found, which is cool, that on an individual player performance level. Yes. That players, regardless of the team, play better when playing at MSG versus other arenas. Now that could be for a bunch of reasons that could be number one, it's. They know it's the limelight. It's New York City. There's more publicity. The, there's more fans they could also understand. And it's kind of a running joke that there's more people on celebrity row. So for. Sometimes there are more models or something like that. So the players want to play better to impress them or just impress celebrities overall. But I think it's really cool that this student was able to find that on an individual player performance level there was statistically significant relationships that they, they do play better.
- [35:16]
Gregory McNiff
Yeah, no, that was definitely one of the more interesting ones. I want to move into more, I guess more unique but also serious content and particularly the school to juvenile incarcerations here. Your student here looked at the data again from open data on crime as well as I guess this peak, this pathways to excellent achievement knowledge and these other programs. Could you talk a little bit about the data set and the conclusion?
- [35:53]
Christian Martinez
Yeah. So this one was, I don't want to say the most serious, but this is a serious topic. And I think that this is based off of early intervention and things that can really be integrated. So they were looking at rearrest data and juvenile cases data. I think there's another data set and they did a great job of coming combining data sets. And again, this is something that I was emphasizing because I want to make sure that I taught students how to think creatively. And part of that is, hey, what data sets can I use? And look, I mean the. This one and the one Regarding New York City restaurants and museums, like a testament to that. So they did find in this one that there was a significant correlation between larger supervision caseloads with higher rearrest rates. And so this is the information that. And obviously we want to get a little more into details if we were to make this more concrete or tangible and useful in the future. But we could take this down and say, like, hey, like, what is this telling us about re arrest data? What is this telling us about different districts and juveniles and how could we use this? And they even looked at like, things like middle school versus high school discharge rates, which is so cool because you're like, okay, like, some people may not even think that there's a difference. And he's like, hey, let's see what we can get.
- [37:28]
Gregory McNiff
Yeah, I thought the map was really nice. And the. Your student references potential intervention points or how to handle these caseloads, maybe to, you know, better. I guess the idea is to obviously lower the discharge rate or the recidivism. So, yeah, I thought it was really, again, another interesting analysis. So I think the, like I said, the one that really, I don't want to say stood out, but was most counterintuitive was this a relationship between mold exposure and domestic violence. So my first question is how. How did the student even think about combining those two data sets?
- [38:06]
Christian Martinez
Wow. Shout out to Shannon, just like all the others, an amazing student. And they, I don't know how this really came about. I wonder if they had mold complaints and that they were worried about. Not like, maybe not even them, but themselves or just were researching mold and like, how it can be, like, rampant in New York City and think like, oh, like, what's the impact? Because you could think, all right, like, there's just a complaint, but what's actually going to happen as a result of the complaint? The way I like to think about it is like, okay, you're. You have potholes in New York City, right? And that is like, oh, it's just a hole in the ground. But is that leading to more accidents? Is that leading to more people dying because of it? And so same thing with mold. Hey, we're complaining about mold, but what's actually happening? Are people getting stressed out? Are people getting influenced, people realizing the effects of mold? And they said that with more months of more mold exposure, they have more domestic violence reports as well. And so there's a lot of ways to think about this. First of all, there are probably too many mold complaints, and mold is a natural phenomenon, right? But you can think about it. Maybe the people are just so stressed out about mold, and it leads to other things, and then they lead to more domestic violence. And I want to say that, like, it's not so much a take on domestic violence. We don't know age or demographics or gender, and domestic violence is incredibly serious of a topic. And if this is a predictor of domestic violence, it's kind of like, hey, maybe we're able to pull the weeds out. You know, you just want to solve domestic violence, but maybe we're not actually figuring out what causes domestic violence. And I'm not also advocating that now we should just abandon everything that we know and go to. Or. She's not advocating for that. We're just saying, hey, this is something that we should look at, because maybe the environment is a predictor of the result, which is a important topic and kind of like why we want to even do research overall.
- [40:22]
Gregory McNiff
Yeah, no, shout out, I think you said, Shannon. I. I don't want to give away the case study, but I just, you know, she scrubs the data really well and even suggests. I want to make sure I get the conclusion right here because she actually says there is a positive correlation between the two, and I think suggests maybe responding more quickly to mold complaints could actually have a positive impact. And I think she says there is a statistically significant positive correlation. Yeah, we see that month by month, domestic violent reports and mold complaints have a moderate positive correlation with one another, and this result is statistically significant. This suggests that mold complaints and domestic violent reports tend to coincide with each other each month. Yeah, that seems like something that's really a. Intriguing and insightful and important and also actionable. I'm just curious, would she follow up on this or present this at the next conference? I think it's in March every year, or what. What. What do you suggest she do with something like this?
- [41:26]
Christian Martinez
Oh, man.
- [41:27]
Gregory McNiff
Yeah, I could put you on the spot.
- [41:29]
Christian Martinez
No, no, no. This is great because Shannon is. And if she ever listens to this, I. I do think you're a fantastic student. And she's always thinking about. All right, what can I do next? Right now, Shannon is actually interested in seeing the relationship between the placement of street cameras. Excuse me, speed cameras in New York City. And I want to make sure I said this right. The relationship between speed cameras in New York City and the amount of deaths caused by crashes in New York City. Trying to see if, like, the before and after of how many crashes were happening in this particular block or neighborhood overall before a speed camera was introduced. And then after a speed camera was introduced. So she is a little busy with that. But I think from this she would love to maybe see about partnering with the Department of Health and trying to focus on getting more of these mold complaints fixed. Especially ones. Well, there's like twofold. It'd be ones that are being complained about a lot of but then also maybe trying to nip as many in the butt as possible as soon as possible. So like is there a way to get a mold complaint complete within a week or a month? And then do we see domestic violence drop?
- [42:49]
Gregory McNiff
Wow.
- [42:50]
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- [43:19]
Gregory McNiff
No, that is like I said, shout out to her and I'm sure we'll be hearing more of her work. Um, you know, I think we the environmental stressors and social complaints in New York City again going back to the quality of life here to see is there some, you know, impact related to I guess the number of noise complaints or flooding. Do you want to talk about that data set? Maybe the conclusions?
- [43:47]
Christian Martinez
Yeah. So Emma is, is very interested in climate change. She also did her thesis regarding. She's gonna kill me if I get this wrong but regarding how people feel about climate change and how stressed and not stressed they are. So it's something I thought it was really cool that she was able to take her interest that she's also conducting her research on and kind of do it in a little different way in a fun way. Like I like there's no. We could just play around a little bit more. So they wanted to see climate change through New York City and kind of measured it through flooding, extreme weather, infrastructure strain and really using flooding as the baseline. Now this is another example of kind of similar to the social infrastructure one of like maybe flooding is not the best metric to identify climate change with and maybe the amount of events isn't the best way to understand social positivity. But this is the data we have and this is what we have to work with.
- [44:55]
Gregory McNiff
Yeah, yeah.
- [44:56]
Christian Martinez
And so the conclusion here is that especially in these urban environments, climate related environmental stressors may influence social behaviors in those environmental urban environments. Now the problem is I like and it's not a problem. Again, we're working with open data, and that comes with limitations. And the fact that we can develop anything with open data is amazing. Regardless. I mean, 3, 1, 1 complaint data is reporting behavior than direct psychological measurements. So that's limitations and similar. I think a lot of my students had this problem of it's not exactly psychological measurements, but I mean, even if you want to look at like an MRI or an fmri, I mean, that's a corollary measure in itself.
- [45:43]
Gregory McNiff
So.
- [45:44]
Christian Martinez
So it's not the first time in science we've used adjacent measurements.
- [45:47]
Gregory McNiff
Yeah, absolutely. Yeah. And she again talks about the idea that we have got a reproducible framework for environmental risk. So really interesting and valuable. You know, we keep talking about New York City. We think of it as an urban area. And I think you earlier said you, you let your students pick whatever they want. Someone looked at the hidden link. Again, this is love to understand how they made the connection here between urban trees and wildlife activity. And I think this student looked at the street tree census and urban park ranger animal addition response. You want to tell us a little bit about this one?
- [46:25]
Christian Martinez
So cool and so different, right? I mean, the fact that my students are able to think of these questions is really such a testament to how creative and resourceful they can be. So actually, one of the most popular data sets on the portal is they did a tree census. I don't quote me. I believe it's 2015. And they love animals, so they're like, hey, we want to see if there's a relationship between the animals and the environment, which makes sense because animal needs a. Animals need habitat just like us, and trees are one of them. So this student was really cool and they were seeing this and discovered that. That there were differences across the boroughs. And the total number of street trees alone does and not. Does not explain where incidents exactly happen. So this is saying that sure, tree placement is impactful, but there's also a gap missing. And that's one of the coolest things about science, is that, like, you can conduct research. It may not be exactly what you wanted or what you envisioned, but it could be such a pathway into what's next. Because now the students can look at, compare a different data set with the urban park ranger animal condition response and see, all right, if it's not trees, maybe it's how close you are to a building or how many people go to this park or something like that. Or maybe it's actually the amount of how close you are to a pond or a river or the water. Something like that, like, there's. There's so many different opportunities.
- [48:12]
Gregory McNiff
Yeah, I thought it was interesting. Your student flags a number of small number of incidents and concentrated a few parks like Van Cortland, Central Park, Prospect park, which suggests that larger parks with extensive natural areas may experience more wildlife activity and a greater likelihood of reported human wildlife interactions. So nice data, nice conclusions there. Okay, I want to go to the last one, which actually again touches on domestic violence and resources to support it. Again, pretty serious topic. Your student, it looks like, pulled the data sets from family violence related snapshots and then annual reports on domestic violence initiatives. What. What sort of analysis? And I should actually, I guess it's Laura here. What analysis did Laura do and what conclusions did she draw?
- [49:06]
Christian Martinez
Yeah, I actually think one of the most impactful things Laura did was actually look at it from a borough to borough standpoint. And I think this is important because New York City is one city, but to some, and I would say maybe me, all five boroughs act independently, have their own personality, own characters, own population. And sure, we're together, but we're also individuals, and I say us as the five boroughs. And so it's important to understand, like, where are actually the problems happening? And so probably I don't want to speak for her, but one of the most impactful things, in my opinion is finding that especially in the Bronx, higher levels of reported domestic violence appear to correlate positively with less access to support services. And like, I think that's important because you're able to identify, like, it's a, it's a grandiose thing to just say, hey, more domestic violence is due to less services, but where are they happening? So now it's saying, hey, she's able to identify. This is the Bronx. So if you want a starting point, this is where we have to go. Because I like to think about it as like, you're taking potential to kinetic energy. That starting is the first part. And if you wanted to really focus on domestic violence and you wanted to proactively prevent this, you could now work on getting more support services in the Bronx and seeing how that can cascade.
- [50:40]
Gregory McNiff
Yeah, no, and that really came across in all your students. It wasn't just high level or a thesis, but it was like you said, data that they drilled down into, they scrubbed. But it was not only actionable, but, you know, clear. And that I should. That borrowed distinction came across in a number of them. I, I really have more sympathy for the Bronx after this interview than I do beforehand. But, yeah, that's what I really Liked, I remember thinking a few of these, you know, their policy initiatives, obviously, like any city, New York struggling with budget and spending and priorities, and something like this can really help it. I mean, to use a, you know, the typical finance from the roi, where do we want to put our resources, both financial and also others, in terms of designing communities like the parks, et cetera.
- [51:25]
Christian Martinez
So, yeah, and on the. The opposite of that, right in that same in Laura, the domestic violence and resource allocation, Staten island was lower, was like the opposite, excuse me, of the Bronx. And you think about it, okay. Like Staten island, some people think is like the stepchild, but maybe there's a difference because of the fact that it's harder to get to Staten Island. So if you're talking about resource allocation, maybe they do a better job of keeping it in house or something like that. Or they're maybe the less connectivity means that they have stronger in house. I'm not exactly sure. But it's interesting to see that Staten island in particular is the opposite of the Bronx.
- [52:04]
Gregory McNiff
Yeah, that's. You nailed it. The visits per 100 incidents. Absolutely. That's interesting. Christian, I'm going to ask you, we talked about your program and your students, but somebody listening to this might say, well, I'm not a musician and I assume your program's a year or two, you know, to switch careers and take this program, but I really like this and I want to understand and maybe even get engaged myself. Is this a. Is the. The R programming language somebody could pick up on their own or at least under, you know, learn a little bit more about without taking a full course? And maybe your own book would be helpful there. And Open Data, is that something the, the everyday New Yorker could engage with?
- [52:47]
Christian Martinez
Yes. So my goal of this course, I've, I'm, I say my goal, but I've had a lot of goals the more I talk about it. But I really wanted to make this as when I thought of reproducible. I wanted my students to be able to reproduce all the things that we did in whatever next chapter they have. I didn't just want it to be siloed. So the first thing I did was I created the New York City Open Data package, which means that anybody using the R language for free can pull in New York City Open Data, any data set. So if you use R, you install ar, you install the package New York City Open Data. You can connect to any data set that's on New York City Open Data. Now, if you know R and you can download, install a package, you can do the same thing with this one. Made it incredibly simple. You don't have to deal with any APIs. You don't need to deal with a separate website. You use it as you normally would. I created the functions as concise and simple as possible. And so if you download R, boom. You can connect to New York City open data in terms of learning R or doing things similar to this, or using New York City open data. I actually created the textbook that was funded by Brooklyn College, reproducible research using R that came about because I signed a textbook. And then when I did my survey, after the course of, hey, what tools did you use? There's no, this isn't about a grade. Like, just help me help you and the next cohort. What'd you use in this class? And all of my students said, the lecture notes, the recordings, I said, okay, like what if I could help my students by putting them all together? And so I was like, okay, that kind of sounds like a textbook. Let me see what I can do. So through a lot of emails, some funding awards, grants, etc. I was able to create the reproducible research in our textbook. Try to make it as simple as possible. I use colloquial wording. I didn't want to make it so crazy. I wanted to make it relatable and a mimicking of the lectures. So I wanted it so if my students in six months get a job at a startup company and they need to run correlation, they could go back to that chapter of the book. Correlations be like, oh, wow. Yeah, I remember doing this because look, the truth is students can be discombobulated. They could forget the recordings, they could have something in the downloads file, something in their documents folder. And now they're searching for code. And like, man, I totally forgot what this even means. And I try, I try to make it so anyone at any time could go to a chapter, figure out what we're doing, follow step by step. And if you are someone that's trying to either get more into this or mimic what we did for New York City Open Data Week and this book, here are all the resources.
- [55:54]
Gregory McNiff
Yeah, that's great. And this is something someone who's not a student, anyone maybe with a technical background or even just passing interest, could, could learn from.
- [56:04]
Christian Martinez
Absolutely. All the materials are free, open. I feel like it would be wrong of me to ask my students to use open data and then not make all of these value resources open as well. So anyone can use all these.
- [56:20]
Gregory McNiff
And I think you can definitely find the book We've been talking about NYC Open Data Student Gallery, Brooklyn College. Just Google that or go to Martinez C1 dash NYC open data. I think I have that right. And Kristen, is there any particular other link they should go to to get either this book or your companion book?
- [56:41]
Christian Martinez
Yes. So kind of like get everything together. We like created the New York City Open Data labs. You can go to NYC open data lab.org and there are a bunch of different resources there. So all of the packages that we created, because I mentioned that we did the New York City Open Data package, but we've actually made it available for Los Angeles, Chicago. I'm forgetting off the top of my head. Oh my goodness. Washington, dc. I think we're trying to do as many open data platforms as possible. So we have about five right now. So information on all those packages. We have the link to the textbook, we have a link to the this book that we're talking about today, the New York City Open Data Student Gallery. My students also created their own portfolios, so I had all of their homeworks. They turned those into books as well. Trying to create like renewable, reproducible things that again, get rid of the, hey, just living in the academic sphere and get it out to the public. So all their personal portfolios are there. We have some articles that we've been writing. We did one about New York City Open Data. I talked about the one that Shannon's currently working on. So a bunch of cool stuff that's. That we all did and will continue to do.
- [58:05]
Gregory McNiff
No, that's great. I mean it's. Yeah. Impactful in a good way. Impacting obviously all New Yorkers and it sounds like multiple other cities. And yeah, really great job. Again, the book is NYC Open Data Student Research Projects in Reproducible Psychological Research, published as an open access volume by Brooklyn College cuny. For listeners interested in exploring the methods that Kristin discussed. The volume is accompanied by an open access textbook, Reproducible Research in R and the NYC Open Data Package. Kristen, thank you so much for joining us today. It was really a fascinating discussion.
- [58:42]
Christian Martinez
Yeah, thanks so much for having me. Thanks. I really appreciate it.
- [58:44]
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- [59:25]
Christian Martinez
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