Have you ever thought about how making music and studying the Earth from space are surprisingly similar? Both involve converting real-world signals into digital data that we can use and understand. ### A Look Back: The History of Analog Signals **Music:** - **Magnetic Tape:** In the mid-20th century, magnetic tape recording transformed the music industry by allowing higher fidelity recordings and the ability to edit and overdub tracks. ![[Pasted image 20240719170931.png]] (A [reel-to-reel tape recorder](https://en.wikipedia.org/wiki/Reel-to-reel_audio_tape_recording "Reel-to-reel audio tape recording") from [Akai](https://en.wikipedia.org/wiki/Akai "Akai"), c. 1978) **Remote Sensing:** - **First Analog Satellite:** The Television Infrared Observation Satellite (TIROS-1), launched by NASA in 1960, was the first satellite to capture images of Earth. It used television cameras to record analog images of weather patterns, marking the start of satellite-based Earth observation. ![[Pasted image 20240719171108.png]] (The TIROS-1 [magnetic tape](https://en.wikipedia.org/wiki/Magnetic_tape "Magnetic tape") data recorder) ### Turning Sounds and Images into Data **Music:** - **Capture with Microphones:** Microphones convert sound waves into electrical signals. This analog signal is then sampled at high rates, turning continuous sound into digital data. - **Analog to Digital Conversion:** The process of sampling converts the sound wave into a digital format, with rates like 44,100 times per second for CD quality. ![[Pasted image 20240719185022.png]] (A flow of audio from sound waves through a microphone to an analog voltage, A-D converter, computer, D-A converter, analog voltage, speaker and finally as sound waves again. <a href="https://commons.wikimedia.org/wiki/File:CPT-Sound-ADC-DAC.svg">Pluke</a>, <a href="https://creativecommons.org/licenses/by-sa/3.0">CC BY-SA 3.0</a>, via Wikimedia Commons) **Remote Sensing:** - **Capture with Sensors:** Satellite sensors detect electromagnetic energy from the Earth’s surface and convert it into electrical signals. - **Analog to Digital Conversion:** These signals are sampled at various resolutions to create digital data, forming detailed images of the Earth’s surface. ![[Pasted image 20240719190510.png]] (satellite communication block diagram. [ref](https://www.mathworks.com/help/satcom/gs/what-is-satellite-communications.html)) ![[Pasted image 20240719190557.png]] (A digital beamforming (DBF) signal chain for a single antenna element. [ref](https://www.microwavejournal.com/blogs/28-apitech-insights/post/35492-advantagesdisadvantages-of-digital-beamforming-in-satellite-applications)) ### The Importance of Resolution In both music and satellite images, resolution is key. In music, higher sampling rates and bit depths mean clearer, more detailed sound. For example, a higher sampling rate can capture even the tiniest details of a sound, making it richer and more immersive. In remote sensing, higher resolution images mean more detail about the Earth’s surface. Smaller pixels in the image can show finer details. Also, the ability to capture different wavelengths of energy (spectral resolution) allows us to identify various materials and conditions on Earth more precisely. ![[analog_digital_raster_high_res.png]] > [!info]- The code used to draw this diagram > ```python > import matplotlib.pyplot as plt > import numpy as np > > # Create a higher resolution waveform and raster grid > def create_high_res_waveform(): > t_high_res = np.linspace(0, 1, 5000) > frequency = 5 # Hz > waveform_high_res = np.sin(2 * np.pi * frequency * t_high_res) > return t_high_res, waveform_high_res > > def create_high_res_digital_waveform(waveform, num_samples=200): > indices_high_res = np.linspace(0, len(waveform) - 1, num_samples).astype(int) > digital_waveform_high_res = waveform[indices_high_res] > return indices_high_res, digital_waveform_high_res > > def create_high_res_raster_grid(size=50): > raster_grid_high_res = np.random.rand(size, size) > return raster_grid_high_res > > # Function to create a sample audio waveform (analog signal) > def create_waveform(): > t = np.linspace(0, 1, 1000) > frequency = 5 # Hz > waveform = np.sin(2 * np.pi * frequency * t) > return t, waveform > > # Create sample waveform (analog signal) > t, waveform = create_waveform() > > # Create high resolution waveform (analog signal) > t_high_res, waveform_high_res = create_high_res_waveform() > > # Create high resolution digital representation of the waveform > indices_high_res, digital_waveform_high_res = create_high_res_digital_waveform(waveform_high_res) > > # Create high resolution raster grid example > raster_grid_high_res = create_high_res_raster_grid() > > # Plot the original and high resolution analog and digital waveform > plt.figure(figsize=(14, 14), dpi=300) > > # Plot original analog signal > plt.subplot(2, 2, 1) > plt.plot(t, waveform, label='Analog Signal') > plt.scatter(t[indices], digital_waveform, color='red', zorder=5, label='Digital Samples') > plt.xlabel('Time') > plt.ylabel('Amplitude') > plt.title('Analog vs Digital Signal in Music (Original)') > plt.legend() > > # Plot high resolution analog signal > plt.subplot(2, 2, 2) > plt.plot(t_high_res, waveform_high_res, label='Analog Signal (High Res)') > plt.scatter(t_high_res[indices_high_res], digital_waveform_high_res, color='red', zorder=5, label='Digital Samples (High Res)') > plt.xlabel('Time') > plt.ylabel('Amplitude') > plt.title('Analog vs Digital Signal in Music (High Resolution)') > plt.legend() > > # Plot original raster grid > plt.subplot(2, 2, 3) > plt.imshow(raster_grid, cmap='viridis', interpolation='nearest') > plt.colorbar(label='Pixel Value') > plt.title('Raster Grid Example in Remote Sensing (Original)') > plt.xlabel('Column') > plt.ylabel('Row') > > # Plot high resolution raster grid > plt.subplot(2, 2, 4) > plt.imshow(raster_grid_high_res, cmap='viridis', interpolation='nearest') > plt.colorbar(label='Pixel Value') > plt.title('Raster Grid Example in Remote Sensing (High Resolution)') > plt.xlabel('Column') > plt.ylabel('Row') > > plt.tight_layout() > plt.savefig('analog_digital_raster_high_res.png') > plt.show() > ``` ### MIDI and Vector Data: Structured Information In music production, MIDI (Musical Instrument Digital Interface) is a way to record and control music. Instead of capturing the actual sound, MIDI records the instructions for creating the sound, like which notes to play, how long to play them, and how loud they should be. This gives musicians a lot of control over their music. #### Sample Table for MIDI Data (for one instrument) | Note | Pitch | Duration | Velocity | | ---- | ----- | -------- | -------- | | C4 | 60 | 1.0 sec | 100 | | E4 | 64 | 0.5 sec | 80 | | G4 | 67 | 0.75 sec | 90 | | C5 | 72 | 1.0 sec | 100 | In GIS, vector data is similar. It represents geographic features using points, lines, and shapes. This structured data is very precise and can be easily scaled and manipulated, much like MIDI in music. Vector data can model complex geographical features and relationships, making it essential for mapping and analysis. #### Sample Table for Vector Data (for one layer) | Feature ID | Feature Type | Coordinates | Attributes | | ---------- | ------------ | --------------------------- | ------------------------------------------ | | 1 | Point | (30.2672° N, 97.7431° W) | Name: "Austin", Population: 950,000 | | 2 | Point | (34.0522° N, 118.2437° W) | Name: "Los Angeles", Population: 4,000,000 | | 3 | Line | ((40.7128° N, 74.0060° W), | Name: "Hudson River", Length: 315 miles | | 4 | Polygon | ((37.7749° N, 122.4194° W), | Name: "Golden Gate Park", Area: 1017 acres | | | | | | ### Coordinate Systems: Mapping Nature in Music and GIS Both fields also share a fundamental need to represent and understand the world in a structured way. **Music**: - **Musical Notation**: In music, we use notation systems to represent the pitch, duration, and dynamics of each note. This helps musicians understand and recreate complex pieces of music accurately. The notation system is a kind of "coordinate system" for music, providing a standard way to interpret and perform compositions. ![[Pasted image 20240719191342.png]] (An example of modern musical notation: Prelude, Op. 28, No. 7, by [Frédéric Chopin](https://en.wikipedia.org/wiki/Fr%C3%A9d%C3%A9ric_Chopin "Frédéric Chopin") [Play](https://upload.wikimedia.org/wikipedia/commons/transcoded/1/1a/Chopin_Prelude_7.mid/Chopin_Prelude_7.mid.mp3 "Play audio")[ⓘ](https://en.wikipedia.org/wiki/File:Chopin_Prelude_7.mid "File:Chopin Prelude 7.mid")) **GIS**: - **Geographic Coordinate Systems**: In GIS, we use coordinate systems to map the Earth. These systems allow us to pinpoint any location using latitude and longitude, just as musical notation helps musicians find their way through a piece of music. Coordinate systems in GIS ensure that we can accurately represent and analyze spatial data from different sources. ![[Pasted image 20240719191607.png]] (A geographic coordinate system (left) measured in angular units is compared to a projected coordinate system (right) measured in linear units (meters) for the same location in the Atlantic Ocean. [ref](https://pro.arcgis.com/en/pro-app/latest/help/mapping/properties/coordinate-systems-and-projections.htm)) ### Processing and Analysis Tools Professionals in both music and GIS employ robust tools to handle and evaluate their data. Engineers in music can edit, mix, and master their songs using programs like Pro Tools, Ableton Live, and Logic Pro X (known as a DAW). They are able to produce the ideal sound, alter levels, and add effects. Experts in GIS and remote sensing can process and analyze geographical data with the help of programs like ArcGIS, QGIS, and ENVI. They may produce maps, categorize photos, and examine spatial patterns to improve our understanding of the outside world. In GIS, commercial off-the-shelf (COTS) software and free/open-source software compete, much as in the music industry. **Music**: - **Commercial Software**: Pro Tools, Logic Pro X and Cubase are industry standards for professional audio work. They offer advanced features and a polished user experience, but they come with a high price tag. ![[Pasted image 20240722125658.png]] (Avid, Pro Tools) - **Open-Source Software**: Audacity is a popular open-source alternative, while Reaper, although not open-source, is an affordable and highly capable DAW that many musicians use. ![[Pasted image 20240722125750.png]] (Reaper. [ref](https://jeffkaiser.com/why-reaper/)) **GIS**: - **Commercial Software**: Esri's ArcGIS Pro is the go-to for many professionals. It provides comprehensive tools and support but can be expensive. ![[Pasted image 20240722125413.png]] (Esri, ArcGIS Pro) - **Open-Source Software**: QGIS is a powerful open-source option. It's free, highly customizable, and supported by a large community, making it a popular choice for those who need robust GIS capabilities without the cost. ![[Pasted image 20240722125910.png]] (QGIS. [ref](https://www.bgeo.es/en/qgis/)) ### APIs: Connecting the Dots APIs (Application Programming Interfaces) play a crucial role in both fields, enabling different tools and systems to work together seamlessly. **Music**: - **VST Plugins**: In music production, VST (Virtual Studio Technology) plugins are a type of API that allows software instruments and effects to be integrated into digital audio workstations (DAWs). These plugins provide a vast array of sounds and effects, enhancing the creative possibilities for musicians and producers, (It's like having a QGIS plugin that also works in ArcGIS Pro. XD ) ![[Pasted image 20240722131104.png]] - **MIDI**: MIDI (Musical Instrument Digital Interface) is another key API in music, allowing different musical instruments and devices to communicate and work together. It records the instructions for creating music, such as which notes to play, how long to play them, and how loud they should be. ![[Pasted image 20240722131125.png]] **GIS**: - **OGC Standards**: In GIS, the Open Geospatial Consortium (OGC) provides a set of standards for geospatial data and services. These standards ensure that different GIS systems and data sources can work together. For example, WMS (Web Map Service) and WFS (Web Feature Service) are OGC standards that enable the sharing of map images and geospatial data over the internet. ![[Pasted image 20240722131142.png]] ### The Art of Interpretation In both fields, data interpretation is key. Music engineers create emotionally powerful sounds, while GIS professionals interpret data to find meaningful patterns and trends, aiding in decision-making for environmental management and urban planning. ### Conclusion: A Symphony of Similarities It's fascinating how making music and studying the Earth from space share so many similarities. Both turn real-world signals into digital data, use high-resolution sampling for precision, and rely on advanced software for processing and analysis. In both fields, there’s a blend of science and art to create meaningful and impactful results. So, next time you're enjoying your favorite song or walking through a well-planned urban area, take a moment to appreciate the technology and creativity behind these experiences. Happy listening and happy mapping! ### Fun Fact: Me Connecting MIDI Controller with maplibre ![[midi-web-map-short.mp4]]