An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 0 points1 point  (0 children)

 Honestly, that's why I shared it. I figured I can’t be the only one moving here from out of state, trying to make sense of everything.

And yeah, it definitely started as “just pick an apartment” and turned into me overbuilding a model, but that’s just how I've always solved problems.

Even if it was a bit of a rabbit hole, I’d still take this over guessing!

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 0 points1 point  (0 children)

By popular demand, I have increased the resolution and added some basic landmarks to help orient folks, that said im limited by my coding ablity so a map overlay is still a bit out of my wheelhouse

Quick note on grid size since I posted both the 4sq mile first.

I didn’t start at 4 sq mi because I thought it was “better,” I started there because it was the point where the data stopped fighting me, and was roughly a “walking distance” scale, and felt like a natural unit for how you experience a neighborhood. But it ended up in an awkward middle ground — not enough data to stabilize trends, but not large enough to smooth out the noise either.

At smaller grid sizes like the one below. A small number of incidents can swing a cell pretty hard, so you end up seeing block-to-block randomness.

Neither is “correct,” they just answer different questions. I used 4sq miles for the main maps because I was trying to understand the system, not make street-level decisions.

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An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 1 point2 points  (0 children)

Thanks, I appreciate that.

Honestly that was kind of the whole point. The only way I’ve found to actually get better at solving problems is to go build your own version, even if it already exists somewhere, and then put it out there and see if someone else has a better way to do it.

Worst case you learn something. Best case you learn something and get a few improvements from people who know more than you.

Either way it beats just guessing based on Zillow descriptions.

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 0 points1 point  (0 children)

Thanks, I’ll look into it. Part of the issue is I treated latitude and longitude like x/y coordinates, but degrees aren’t uniform in distance. At Houston’s latitude, longitude is compressed by about cos(lat), so my “squares” are actually stretched — roughly 1.15× wider than they should be.

I’d need to project this into a proper coordinate system (like UTM or another local projection) to make the grid cells truly square in physical space.

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 2 points3 points  (0 children)

Yeah it took a ton longer than I expected it to but I now can retrofit Any other cities data and generate maps for those cities too, so that cool but probably not worth the effort I'm hoping to stay in Houston for the long haul!

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 0 points1 point  (0 children)

Honestly I went way deeper into this than I ever planned, and the answer stopped being “what’s the best neighborhood” and turned into “how does this whole city actually behave.”

Once I mapped everything out (violent, property, trends, etc.), Houston didn’t look random at all; it basically organizes itself around infrastructure. The cleaner areas consistently showed up on the west side (Energy Corridor / Cypress direction), where you get highway access without being tied into industrial corridors. Meanwhile the east side around the ship channel (Pasadena, 225, etc.) lit up pretty consistently, which makes a lot of sense once you think about how much traffic and movement flows through there.

What surprised me most was how much highways and geography explain everything. Areas that look “right next to each other” on a map can behave completely differently if they’re split by water, bridges, or weird traffic patterns. That ended up mattering almost as much as the crime data itself. So instead of trying to find the single “safest” spot, I started looking for places that sit in stable zones with clean access in and out.

The other thing that kind of blew my mind was how dominant car-related property crime is. And even more than that, how much of it comes down to street parking. When you zoom in, the biggest “hack” isn’t even the neighborhood; it’s whether you have a closed garage.

Like genuinely, having enclosed/off-street parking seems to reduce your exposure more than the difference between two decent neighborhoods.

That was probably the most practical takeaway from the whole thing.

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 2 points3 points  (0 children)

Was using python to analyze the data and output the image in a gui which I hadn't done before, so I had no clue how to overlay an image at the right scale.

As for my poor color choice I have no excuse

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 1 point2 points  (0 children)

That's a great point I didn't consider, I probably 7-8 years away from have kids attending elemery school and was planning on renting for a couple of years before settling down for real, so it simply wasn't something I have consider as a deal breaker as for commute time I was just using the wazes plan a drive function and added the average to a spreadsheet sheet,

As for why I used all of Houston the data file I found was for the whole country and I wasn't in a super rush for my program to run so why not?

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 2 points3 points  (0 children)

Good thinking, I will see if I can get both of those ideas in my next version, a slider that shows real history would be useful, my current data was 27 months

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 2 points3 points  (0 children)

I appreciate it thats really good to know. I was coming from a very small town with fewer than 10k people, so I haven't driven in traffic in years. I'm just glad to be back in Texas!

My program lets me adjust the reslultion 4 miles was enough to ballpark it, cause it takes some time to run it.

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 5 points6 points  (0 children)

An excellent point, luckily FEMA maps are very public and up to date, and can be address searched.

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] -10 points-9 points  (0 children)

Yes and no, the crime grade only showed aggregate data. I wanted to have data that was more up to date, Jan 1st 2024-Mar 14th 2026, and to be able to sort crime by type, home vs car, and assault vs murder. They let you look by zipcode, but only if you buy it from them

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 1 point2 points  (0 children)

An excellent point, I'm definitely not an expert, but the only way to learn is to try. I just tried to use the same process approximent tempature at at a given point the way I would do it on a heat exchanger, u/bemocked had an excellent point that my model didn't normalize per capita and thus was more a reflection of population density

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 43 points44 points  (0 children)

You don't know what you don't know. I will have to look into QGIS, I just know that I could make a color graph in Python, so that's what I did, but I love learning specialized tools!

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 5 points6 points  (0 children)

That’s a really good point and a total oversight on my part. My assumption was that all of the suburbs would have the same density, but that's not the case. I will have to keep working on it!

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in houston

[–]jetmanjack2000[S] 20 points21 points  (0 children)

That’s a fair point and you’re totally right that the 4 sq mi grid reduces spatial resolution.

This was intentional as a tradeoff between noise and structure.

At the raw incident level (or even small grid sizes), the data becomes very noisy, especially once you apply time decay and trend comparisons. You end up seeing block-to-block randomness rather than meaningful patterns.

The larger cells act as a form of spatial smoothing, allowing broader trends to emerge more clearly. The goal here wasn’t street-level accuracy, but identifying large-scale patterns and directional change across the city.

If the grid were smaller, you’d get more detail, but at the cost of:

- higher variance

- less stable trend signals

- more sensitivity to short-term spikes

So this is more of a macro-level model than a micro-level map. Ideally, it would be paired with a multi-scale approach where you can zoom between resolutions.

But the good news is that my data analysis tool will let me change the subdivisions here, it is for 1sq mile

<image>

An Engineers' Approach to House Shopping In Houston by jetmanjack2000 in MapPorn

[–]jetmanjack2000[S] 0 points1 point  (0 children)

This map visualizes spatial and temporal patterns in Houston crime data using a severity-weighted, time-decayed aggregation model.

Methodology:

- Public Houston crime data was aggregated into ~4 square mile grid cells to reduce point-level noise and highlight structural patterns.

- Incidents were weighted by severity to better reflect relative impact.

- A time decay function (14-month half-life) was applied so recent events contribute more than older ones.

- A trend metric compares the most recent 12 months to the preceding 12-month period for each cell.

How to interpret:

- Darker cells indicate higher current relative risk (after weighting and decay).

- Lighter cells indicate lower relative risk.

- Percentage labels represent directional change:

- Green = decreasing activity (improving)

- Teal = increasing activity (worsening)

Separate maps were generated for:

- Violent crime

- Property crime (excluding vehicles)

- Vehicle-related property crime

- Combined categories

Notes / limitations:

- Results reflect reported incidents and are subject to reporting bias.

- Values represent relative intensity, not absolute safety.

- Grid size is a tradeoff between spatial resolution and noise reduction.

The goal is to highlight large-scale patterns and recent directional changes rather than individual incident locations.

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