Backend GIS Database cleaning and modeling + wind dispersion algorithm - Environmental monitoring.


Deliverable - Backend database cleaning, modeling

Skills required: R, GIS knowledge, Python, Some Data Engineering

Automate the backend process of cleaning and aggregating different data sets.

Once data is cleaned and in one database there are five key steps:

1) Convert Legal Land Descriptions of well sites to GPS co-ordinates

One potential option:

[login to view URL]

2) Deduct wind speed and direction from vehicle speed

(methane sensor is mounted on vehicle)

3) Run wind dispersion algorithm (This can be a manual step for this project)

[login to view URL]

4) Output results of wind dispersion algorithm onto a map visually represent wind dispersion and concentration levels. (does not need to be heavily designed or visually impressive at this stage)

A RFP with more details of the project to be shared with selected candidates.


Currently, it takes a person 4-5 days to manually go through all the steps above. Although the end goal for this is full automation a few manual steps such as to initiate the wind dispersion algorithm is fine for this first phase.

Goal is to get the time down to a few hours.

Introduction :

This request for proposal is an invitation to a select group of companies to submit proposals for a project to create a minimum viable product (MVP) that can transform raw methane emissions data from natural gas producers into actionable insights. This project will be established as “MethaneChain - Minimum Viable Product”.


In this use case emission data is being acquired by a sensor on a truck that drives around collecting GPS, Atmospheric, wind and CH4 emission data in parts per billion.

Currently the process of moving this data around is done manually or with very basic “R” scripts and it can take one person several days to scrub, prep and analyze the data.

The current users come from environmental engineering or university research projects and do not have the computer science expertise to speed up this process. This causes this valuable emission data to go unused until it is often too late to be useful.


The goal of the MVP is to reduce the time of this laborious data process to less than an hour.

However the entire process does not need to be automated at this stage. A few manual steps are allowed as necessary to reduce costs. Data workflow.

Intent of RFP:

The intent is to obtain information leading to the selection of one (1) or more vendors to participate in this project. Through this process, MethaneChain also seeks to develop operational relationships with development vendors who can support our vision.

Appendix A - Workflow

A. Import Raw Data (Back-End)

Import wellhead / facility location and emission data, and associated site IDs

Data from publically available datasets

Clean and aggregate public data

Import raw emissions sensor and associated GIS data

Clean data and sort into standardized fields

Import environmental (wind, temperature and pressure) data

Data sources include Environment Canada and local airport METAR measurements, among other potential sources

Clean environmental data and sort into standardized field

Import current price per volume of natural gas and other regulatory thresholds (i.e. allowable baseline emissions)

Combine raw data into a new database

Data should be geolocated to individual sites or regions depending on data type

B. Calculate Parameters (Back-End)

Run wind dispersion algorithm using site locations as centre points to estimate emissions downwind of sensors

Estimate emission rate (i.e. volume per day and volume per year) and leak cost (i.e. dollars/year) for sites using dispersion results and current spot price of natural gas

Calculate a rough estimate of the carbon tax liability or potential credit (as compared to baseline) for site.

C. Visualizations and Reporting (User Interface)

Clean and format calculate data for export and visualization

Load data into visualizations

Beceriler: Python, Veri Madenciliği, Big Data Sales, SPSS İstatistik, İstatistiksel Analiz

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İşveren Hakkında:
( 1 değerlendirme ) Los Angeles, United States

Proje NO: #21153871

Bu iş için 11 freelancer ortalamada $13/saat teklif veriyor


Hello sir. I have checked your project description carefully and understand it well. I have good experience in python and data engineering. I look forword to discuss in detail. Thank you.

$10 USD / saat
(23 Değerlendirme)

Hi, Client. Nice to meet you I'm very interesting and confident on your project. Because I'm very good and backend development for data modeling and data analysis. More important thing is that I've a lot of experiences Daha Fazla

$20 USD / saat
(2 Değerlendirme)

Hello, I am a data scientist and R/Python programmer. I have read your description, and I would like before proceeding to thank you for the level of detail you have provided. It is good to see employers take the time t Daha Fazla

$15 USD / saat
(15 Değerlendirme)

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$12 USD / saat
(2 Değerlendirme)

Thank you for your efforts. Great. Let's go with us. --------------------------------------------------------------

$15 USD / saat
(7 Değerlendirme)

1.I am an expert in python, Statistics, regression analysis, using both Excel and SPSS, also expert in Excel, Excel formulas, and all excel functions, macros, lookup, pivot tables and charts. [login to view URL] done many projects Daha Fazla

$12 USD / saat
(9 Değerlendirme)

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$12 USD / saat
(4 Değerlendirme)

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$12 USD / saat
(6 Değerlendirme)

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$12 USD / saat
(4 Değerlendirme)

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$15 USD / saat
(6 Değerlendirme)

Hello, I am interested in your project, would you like to discuss further details with me! I will wait for your reply. Thank you. Regards,

$12 USD / saat
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