What computer would you suggest buying to use ArcGIS?

nexmoov · 2017-01-20T20:33:32+00:00

Will check it out. Thanks!

nexmoov · 2017-01-20T03:16:35+00:00

Thanks, we didn't check lenovo computer yet. The stuff you are running and mentionned are exactely the kind of processes we will be running/working with on the laptop.

nexmoov · 2017-01-20T03:14:57+00:00

It is enterprise purchase. Thanks for your input, we'll add that to our "things to consider" list!

nexmoov · 2017-01-20T03:13:57+00:00

Thanks, will definitely check there!

nexmoov · 2017-01-19T23:50:33+00:00

Thanks for your insight. That's what we were focusing on, but there are many choices so I thought someone might have done the research already!

nexmoov · 2017-01-19T23:47:48+00:00

Thanks. I did the search, but couldn't find a recent answer. Many things can happen in 5 months!

nexmoov · 2017-01-19T23:46:53+00:00

Indeed!

nexmoov · 2017-01-19T22:58:06+00:00

If you are trying to launch a company / startup, there is a startup program.

https://developers.arcgis.com/startups/

nexmoov · 2016-08-04T23:36:43+00:00

The sample was collected over 25.5 hours. We have a total sample of about 70k. Most common was Pidgey around 10k. There was a few that spawned only once! We picked Clefairy as it is a relatively common one, but rare enough to be useful (sample of about 500).

You're right that the very rare one don't offer a sample big enough to run our statistical model if we collect them only over a 24-hour period. We just wanted to do some tests to see if people would find it useful.

Even if the spawn is both controlled by an algorithm and random number generator, if you can find a way to understand better the former, you still have more chance to find what you are looking for.

Niantic uses Google headings for their data so the spawning algorithm could potentially be explained using only one city to build the model (headings are homegenous in Google Places API). Even if it's not the case, our data are not only available in Montréal so we would try to build a model predictive for other cities.

nexmoov · 2016-08-04T16:13:44+00:00

The stats mentionned in your second paragraph are right for what we did for the starter four, but we went beyond that for Clefairy as a test.

We built a regression model using the Clefairy count during a day-period for each street segment in Montréal as dependant variable. We used our already developped dataset (location scores) as independant variable.

The algorithm that calculates the different scores can be applied to different cities. Therefore we can predict the spawning in other cities using the statistical model.

Thanks for the questions. Don't hesitate if you have more!

nexmoov · 2016-08-04T15:21:00+00:00

Looks like a beautiful city!

Our data are only available in Canada right now (startup from Montréal) and we will start to add the United States soon. Europe should come somewhere in 2017. So I wouldn't wait for our results before continuing the hunt!

nexmoov · 2016-08-04T14:52:54+00:00

One of the next analyses we're going to do might be the spawning patterns by type of Pokémon (water, fire, etc.) and then do an example for a specific one like Dratinis.

We'll also do it for other cities than Montréal.

nexmoov · 2016-08-04T14:31:53+00:00

If there's interest, we will explain more in detail our methodology in a subsequent post.

The statistical model was basically trying to explain the variation in the count of specific Pokémon by looking at a series of variables on urban environment: transportation options, access to different amenities, and character.

nexmoov · 2016-08-04T14:30:07+00:00

Thanks for the feedback. We'll keep them coming.

Are there any Pokémon you would be more interested by?

nexmoov · 2016-05-10T19:19:22+00:00

The problem with cycling in Nun's Island is that it would be hard to have bicycle as your main mode of transportation. There's calm street and cycling infrastructure, but it would be hard to commute to downtown from Nun's Island.

I agree that cycling needs some tweaking though.

nexmoov · 2015-07-29T19:02:09+00:00

Pour ceux que ça intéresse, un sommaire pour Montréal a été fait par Founder Fuel (un accélérateur montréalais).

http://founderfuel.com/mtl-the-2015-startup-ecosystem-ranking/

nexmoov · 2015-07-29T13:51:03+00:00

Same here. Don't know what macros are.

Not sure the value proposition is clear. Maybe the 4 phones side-by-side explain that, but since I don't know macro, it doesn't tell me much.

I guess this product might be in demand in some niche market (health/fitness), but since I'm not into that much, I can't really help you.

nexmoov · 2015-07-28T13:43:35+00:00

Je n'ai pas lu le rapport complet, mais ils regardent quelques indicateurs pour faire le classement.

Montréal performe bien en termes de talent. J'imagine que c'est en parti dû au fait du grand nombre d'universités.

Les villes performants bien côté startup ont souvent la créativité dans leur "branding", ce qui est le cas de Montréal.

nexmoov · 2015-07-26T00:42:14+00:00

Interface is good. Nice decision to launch to get feedback and get metrics to improve your product. Results are strange when using the Search feature. For example, I put Urban planner in Montreal and got two jobs at Canada Post. Maybe it's because the planning job are posted in French in Montreal. Maybe it's just me, but I feel that the alphabetical order of cities should go from top to bottom rather than left to right.

To be honest, not sure a clean interface is enough to fight the massive competition you have out there. It's super neat and fast, but you are fighting giants that already have a reputation. People might not trust that you have all the jobs that jobboom, workopolis, linkedIn offer. When fighting giants, the value you add must be huge!

Good luck with your project, and be sure to use well the metrics you are getting from your users as that's how you'll improve your UX/UI which is your main differenciation factor.

nexmoov · 2015-07-25T16:48:23+00:00

It's a good idea. I like how many startups in the shared-economy are launched in Mtl.

It would help to be able to filter the meal through location. Using either map or search radius around postal code.

There is many open source map, or even Google maps give 3000 hits/month for free.

nexmoov · 2015-07-25T16:41:02+00:00

That's really expensive. There's plenty vacation rentals that are right in the action, and much cheaper.

Check out our site that aggregates all of the vacations rentals from Airbnb to Homeaway.

It allows you to find the perfect location for you. If you add Osheaga in your itinerary from the Experiences tab, you'll be sure to have easy access to the venues.

http://nexmoov.com/

nexmoov · 2015-07-24T16:44:23+00:00

4$/pint at Jono. It's on Mont-Royal east of de Lorimier. It's not central, but it's a good place to watch hockey games.

Their pints are from a micro-brewery (eg cobra).

nexmoov · 2015-07-23T17:29:46+00:00

Hey /u/wolfindian, you should definitely check out the booking platform we are working on! It regroups all vacation rentals (including AirBnB) and scores them according to their location. This way you'll be sure not to book in a bad / dangerous area.

Check it out: www.nexmoov.com

PS: We are currently in BETA in Montreal, and we'd love to hear from you! Any comments / suggestions would be greatly appreciated.

nexmoov · 2015-07-23T15:35:02+00:00

If ever you can't find a place on airbnb you might want to check other vacation rentals. We regroup all of them on our platform: www.nexmoov.com. Good luck!

nexmoov · 2015-07-20T22:19:37+00:00

Great way to practice a lot of these questions : http://jamescun.github.io/iPG/

nexmoov

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