---
tags: mdef
---

# MDEF - A world in data 2022 (internal)

:::info
**Tools Demos**
https://hackmd.io/i46Hmb2vTcKI6_DIoaxDvQ

**Class Notes**
https://hackmd.io/DSaKPIdBRhCTiEa8JAdbKA
:::

Measuring the world introduces students to the concept of a world in data by designing artefacts to measure their daily analogue and digital activity. The fundamental aspect is to understand nowadays data-driven world from the sourcing, that could range from a temperature sensor to an Instagram like, postprocessing, storage and consumption. It aims to work both as an introduction to some key concepts behind physical computing to support the _Machines that Make_ but more importantly as an introduction to the idea of information which is critical to the _Extended intelligence_ module.

[TOC]

## Preliminary Schedule


## Conceptual framework


## Structure

- [Introduction](#0-Introduction)
- [From objectives to hypothesis](#1-From-objectives-to-hypothesis)
- Define objectives
- Make questions
- Create hypothesis
- [From hypothesis to data](#2-Hypothesis-to-data)
- Data in an ideal world
- Tools Demos
- Data in the real world
- Reverse-auction
- [Data capture](#3-Data-capture)
- Limits and minimum requirements
- Data capture strategy and flight check
- Data sharing requirements
- [Evaluation tools](#4-Evaluation-tools)
- [Presentation](#5-Presentation)

---

### 0. Introduction

:::info
:clock3: (1-2 hrs)
**Presentación + Discussion**
:::

1. **Global description**
2. **Showcase real life example cases**: Plaza del sol, covid case detection (thermal cameras, certificates, etc), big startup example (airbnb, globo, uber, google, etc), human habits through garbage. (Strava military case)
3. Interactive discussion on **data gathering technologies** around subjects like:
* Big data
* Data fusion
* Privacy and control
* Hypothesis and bias
* High <> low tech discussion
* Data proxies, inference, indirect data gathering

**Global scheme**

- Planteamos pregunta
- Planteamos hipótesis que permita responder pregunta
- Planteamos qué datos nos ayudarían a probar esa hipótesis
- Diseñamos la técnica de captación de datos
- Evaluamos los datos y si prueban la hipótesis

---

### 1. From objectives to hypothesis

:::info
:clock3: (1 hr)
**Dinámica grupal**
:::

After this activity each group should have a clear and concise hypothesis to work with.

1. **Objective**: Motivation about your personal or collective everyday activity, find a specific objective, something you want to achieve or change. - _I want to live next to my workplace_
2. **Question**: From those objectives, make questions that are related and help you understand or act upon them: _Am I the only one that can't live next to my workplace?_, _Am I poor?_, _Are the house prices too high?_
3. **Hyphotesis**: Convert that into an hypothesis. _I am the only one that can't afford an apartment next to my workplace_ vs. _The majority can't afford it_

---

### 2. Hypothesis to data

:::info
:clock3: (2 hr)
**Dinámica grupal**
:::

Try to bring the hypothesis you presented earlier to a point where you can assess them with the tools we provide you with:

1. **Data in an ideal world**: what are the metrics I could use in an ideal world? _Salaries_, _house prices_, _housing park status_
2. **Causes and consequences**: what are the causes that relate to those metrics? _people are unhappy_, _houses are crowded_, _people get late to work_...
3. [**Tools Demos**](https://hackmd.io/i46Hmb2vTcKI6_DIoaxDvQ): introduction to the available technologies proposed
4. **Data in the real world**: what are our actual real metrics we can measure after the demo?
5. **Reverse-auction**: To asign the offered technologies, every team has to present arguments on why the other teams can take advantage of the technologies. In the cases where there is no agreement, tools will be randomly assigned.

### 3. Data capture

:::info
:clock3: (2-6 hr)
**They do**
:::

Let's measure with the tools we learnt:

- **Limits and minimum requirements**: we explain the minimum requirements and the maximum time the can use for this
- **Data capture strategy and flight check**: they prepare a capture strategy and we check it
- **Data sharing requirements**: they make sure their data is accessible and they share it afterwards.

### 4. Evaluation tools

:::info
:clock3: (1 hr)
**Presentation**
:::

How to test a hypothesis using the proposed tools. Consolidate data on a spreadsheet: from data to information, to knowledge. Basic statistics methods and topics: i.e. average, outliers, "How to lie with a chart?"

### 5. Presentation

:::info
:clock3: (1 hr)
**They do**
:::

Present your experiment and your process step by step. What failed? What were you expecting that didn't happen?
* Present a plot or some kind of visual representation of the gatered data.
* Make use of other data?

### 6. Tutorial

Document your tools for others to replicate your experiment and use them further during the course

## [Proposed](https://hackmd.io/i46Hmb2vTcKI6_DIoaxDvQ) tools

### Used

#### 1. SCK

Arduino sensor

#### 2. Pi Time Lapse

https://elinux.org/RPi-Cam-Web-Interface
https://piwebcam.github.io/
https://pimylifeup.com/raspberry-pi-time-lapse/

#### 3. Web Scraping (or Social Media API)

- Jupyter example from code club http://arman.najari.fabcloud.io/covid-19-map/ , https://gitlab.fabcloud.org/arman.najari/covid-19-map
- Example on BBC news https://fablabbcn-projects.gitlab.io/learning/fabacademy-local-docs/clubs/codeclub/pythoninternet/#interacting-with-the-internet
- API example https://fablabbcn-projects.gitlab.io/learning/fabacademy-local-docs/clubs/codeclub/pythoninternet/#api-requests

#### 4. Smartphone as a GPS

https://owntracks.org/ or [nexttracks](https://codeberg.org/nexttracks/android) (free version)
http://www.funf.org/journal.html
https://gpslogger.app/ (best?)

#### 5. Survey Domestic Style

#### 6. Arduino as a Sensor

### For another time

#### ESP8266 Wi-Fi Counter (beacons)

You will need an ESP8266 for this. Clone the repository from:

git clone [e-mail]:kalanda/esp8266-sniffer.git

And get the outputs as (number of WiFi probe requests available):

RSSI: -69 Ch: 12 Peer MAC: fc:f5:c4:90:9d:f0 SSID: Iaac-Office-Wifi
RSSI: -83 Ch: 13 Peer MAC: e6:b5:06:98:45:3d SSID:
RSSI: -85 Ch: 13 Peer MAC: e6:b5:06:98:45:3d SSID:
RSSI: -82 Ch: 13 Peer MAC: a6:17:be:01:8b:dc SSID:
RSSI: -85 Ch: 13 Peer MAC: 52:8e:49:fa:f8:7c SSID:
RSSI: -69 Ch: 13 Peer MAC: 2c:33:7a:f0:44:5d SSID:
RSSI: -69 Ch: 13 Peer MAC: 2c:33:7a:f0:44:5d SSID:
RSSI: -79 Ch: 1 Peer MAC: 00:7e:56:6a:69:86 SSID: 11n-AP

#### Recorder and audacity

...

#### PH Cabbage or analog DIY sensing

(find similar options)

#### Historical Data Sources

...