I want to build a website with a map based on openstreetmap that colors buildings based on a their potential average annual yield of solar power. I have the energy data for individual houses.
My question is now, can I assign each house (identified by street name and number) a value and the house can then be colored based on this value in the browser?
I have little to no experience with openstreetmap and would be happy about hints into the right direction.
So you need a OSM dataset and filter it for building=* ways to get the building outlines (e.g. with osmosis). Then you do create a second run to filter for addr:= tags of nodes and merge them with the building outlines from step 1. Be aware of conflicts and that one building can have multiple addresses. So now you have a dataset with normalized addresses and need to create a lookup structure like hashmap to get a mapping for your solar data: addr:street x addr:housenumber -> building id
(very raw idea on how to do it)
IMHO the mixing of external datasources to the copyleft open database license makes that you need to relicense your dataset also under ODbL.
Also keep in mind that not every address is currently at OSM and the existing ones can be wrong!
Related
I have two questions
the first one is:
I have large data come from the server I saved it in a list , the customer can filter this data by 7 filters and two by text watcher this thing caused filtering operation to slow it takes 4 seconds in each time
I tried to put the filter keywords like(length or width ...) in one if and (&&) between them
but it didn't give me a result, also I tried to replace the textwatcher by spinner but it's not
useful.
I'm using one (for loop)
So the question: how can I use multi filter for list contain up to 2000 row with mini or zero slow?
the second is:
I saved from 2 to 8 pictures in the server in string form
the question is when I get these pictures from the server how can I show them in high quality?
when I show them I can see the pixels and this is not good for the customer
I don't want these pictures to take large space in the server and at the same time I want it in good quality when I restore them to display
I'm using Android/ Java
Thank you
The answer on my first quistion is if you want using filter (like when you are using online clothes shop and you want to filter it by less price ) you should use the hash map, not ordinary list it will be faster
The answer on my second question is: if you want to save store images in a database you should save it as a link, not a string or any other datatype
I am using the Weka GUI for classifying sensor data.
I have measures of 10 people, the data is sorted. So the first 10% correspond to participant 1, the second 10% to participant 2 etc.
I would like to use 10 fold cross validation to build a model on 9 participants and test it on the remaining participant. In my case I believe I could accomplish this by simply not randomizing the data splits.
How would I best go about doing this?
I don't know how to do this in the Explorer.
In the KnowledgeFlow GUI, there is a CrossValidationFoldMaker used to create cross-validation folds. This has an option to Preserve instances order, which says it preserves the order of instances rather than randomly shuffling.
There's a video describing the KnowledgeFlow interface here:
https://www.youtube.com/watch?v=sHSgoVX9z-8&t=7s
Building a LAMP services application that will have 10000's of Vendors providing delivery to Customers, and upon the Customer entering their address, we need to generate a delivery list of Vendors which can provide service to that location. Each Vendor will have a delivery boundary that will be defined by one of these three criteria:
A. List of Zip Codes
B. Distance of delivery point from Vendor in miles (X) (point to point)
C. Defined polygon drawn (most likely) in GME and imported as KML (point in polygon)
A is straightforward, but after extensive research we are unsure of what would be the most efficient and scalable way to approach B and C. Should we use MySQL to store the data and calculate results using code/classes/library, or should we setup a spatial DB like PostGIS to handle all geo storage and calculation, and what about API solutions for some or all, etc.?
Here is our current line of thinking in broad strokes:
Store polygon data (as KML?)
Convert Vendor address to verified lat/long coordinates
Convert B and C boundaries to zip code array to generate subset of likely matches
Convert Customer address to verified lat/long coordinates
The algorithm would then have 3 parts to return a master delivery list:
Part (a):
Query all A Vendors who deliver to Customer's delivery zip code
Part (b):
Filter out all B Vendors that don't have the Customer's zip in likely match array
Query that subset of B Vendors where the distance between coordinates is less than specified
Part (c):
Filter out all C Vendors that don't have the Customer's zip in likely match array
Query that subset of C Vendors where Customer's coordinate is within the polygon
Seeking advice on best practice and what tool/technology/APIs to use, for each step starting with address verification, long/lat of the verified addresses, auto-generate zip array based on spatial data of B and C, calculating point-to-point, creating polygon, storing/converting polygon data, using KML or what?, and calculating point-in-ploygon. Pointers to posts/research/resources very welcome!
Suppose I want to do some data mining on the database of a supermarket. What does that actually mean?
1) What will the output/results be like?
2) Will the output be different every day or change over time?
3) Before applying data mining, do I need to know what I want or will data mining give everything I want automatically?
Data Mining is a general category of techniques that can be applied to different kinds of datasets, just like programming is a general category of techniques that can be applied using different languages to do different things.
None of your questions make any sense.
A1: Data mining will give us an accurate reports about your queries of database of supermarket.
A2: Sure, because Data mining depend on analyzing during time, in this case it depend on your problems or goals that you want to reach it. if your database was very big also you built data warehouse in right way you will get the different output over time.
A3: yes you should determine what are the problems you have to mine then use tools of Data mining to get the results or indicators automatically.
To answer your first question: For the case of supermarket customer data, I could image the following questions:
how many products X are usually sold on Fridays ?
(helps you to determine how many X you should have in stock)
which customers bought product X often in the last month/year ?
Useful when when you introduce a new X-like product: send advertising material (which has a given cost) only to those customers.
given a customer buys product X (e.g. beer) what's the probability that he/she also buys product Y (e.g. chips) ?
useful for the following: make sure X and Y never are on promotional offer at the same time (X and Y are bought together often). Get the customers into the store by offering a rebate on X knowing they'll also by Y at the same time. Or: put a high price X-like product right next to Y, putting the cheaper X somewhere else.
which neighborhoods have the smallest number of customers ?
helps to find out which neighborhoods you could target with advertising to bring more customers into the store.
Often, by 'asking certain questions to the data' one discovers some features and comes up with new questions.
Data mining is a set of techniques. It refers to discovering interesting and unexpected patterns in data.
If you want to apply some data mining techniques, you need to know which one and you should know why. The answer to questions 1, 2 and 3 depends on the techniques that you choose.
For example, if i want to find associations between items sold in a supermarket, i may use association rule mining. If i want to find groups of similar customers, I might use a clustering algorithm. etc.
There is not just ONE technique in data mining.
Having implemented an algorithm to recommend products with some success, I'm now looking at ways to calculate the initial input data for this algorithm.
My objective is to calculate a score for each product that a user has some sort of history with.
The data I am currently collecting:
User order history
Product pageview history for both anonymous and registered users
All of this data is timestamped.
What I'm looking for
There are a couple of things I'm looking for suggestions on, and ideally this question should be treated more for discussion rather than aiming for a single 'right' answer.
Any additional data I can collect for a user that can directly imply an interest in a product
Algorithms/equations for turning this data into scores for each product
What I'm NOT looking for
Just to avoid this question being derailed with the wrong kind of answers, here is what I'm doing once I have this data for each user:
Generating a number of user clusters (21 at the moment) using the k-means clustering algorithm, using the pearsons coefficient for the distance score
For each user (on demand) calculating their a graph of similar users by looking for their most and least similar users within their cluster, and repeating for an arbitrary depth.
Calculating a score for each product based on the preferences of other users within the user's graph
Sorting the scores to return a list of recommendations
Basically, I'm not looking for ideas on what to do once I have the input data (I may need further help with that later, but it's not the point of this question), just for ideas on how to generate this input data in the first place
Here's a haymaker of a response:
time spent looking at a product
semantic interpretation of comments left about the product
make a discussion page about a product, brand, or product category and semantically interpret the comments
if they Shared a product page (email, del.icio.us, etc.)
browser (mobile might make them spend less time on the page vis-à-vis laptop while indicating great interest) and connection speed (affects amt. of time spent on the page)
facebook profile similarity
heatmap data (e.g. à la kissmetrics)
What kind of products are you selling? That might help us answer you better. (Since this is an old question, I am addressing both #Andrew Ingram and anyone else who has the same question and found this thread through search.)
You can allow users to explicitly state their preferences, the way netflix allows users to assign stars.
You can assign a positive numeric value for all the stuff they bought, since you say you do have their purchase history. Assign zero for stuff they didn't buy
You could do some sort of weighted value for stuff they bought, adjusted for what's popular. (if nearly everybody bought a product, it doesn't tell you much about a person that they also bought it) See "term frequency–inverse document frequency"
You could also assign some lesser numeric value for items that users looked at but did not buy.