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fmorenog/README.md

Fabián Moreno Garza

Spatial Data Science · Urban Analytics · GIS

MSc Urban Analytics, University of Glasgow · BArch, Tecnológico de Monterrey

I use spatial data, Python and GIS to study urban and regional problems. My recent work has examined neighbourhood deprivation change, public transport reliability, urban environmental conditions and travel behaviour, using methods ranging from spatial statistics and GIS to machine learning.

Before specialising in urban analytics, I worked in data consultancy and business systems across retail, healthcare, manufacturing and environmental engineering.

What I work with

Spatial & GIS: ArcGIS Pro · GeoPandas · PySAL · Shapely · QGIS (developing) Python & Data: Python · Pandas · scikit-learn · DuckDB · SQL Visualisation: Tableau · Power BI Workflow: Git · GitHub

Featured projects

🏙️ Predicting Neighbourhood Deprivation Change in Glasgow

Investigating whether neighbourhood deprivation change across Glasgow's 746 data zones can be predicted from socioeconomic, housing and built-environment characteristics. The project uses spatial statistics, machine learning and geographically separated model validation to examine how well predictive models generalise across the city.

🚌 Bus Headway & Reliability Analysis — Manchester

Analysis of approximately 310,000 GTFS-Realtime vehicle-position records along Manchester's Oxford Road/Wilmslow Road corridor, examining how bus performance and reliability vary spatially along the route.

🌫️ PM2.5 Prediction with Graph Neural Networks — London

Graph-based modelling of PM2.5 air pollution across 25,053 London Output Areas using GraphSAGE, PyTorch Geometric and a KNN spatial graph constructed from neighbourhood relationships and census characteristics.

Get in touch

📧 fabian.morenog@gmail.com 💼 LinkedIn 🌐 Portfolio

Pinned Loading

  1. Fabian-MorenoFabian-MorenoPublic

    About me

  2. glasgow-neighbourhood-decline-mscglasgow-neighbourhood-decline-mscPublic

    Predicting neighbourhood deprivation change across Glasgow

    Jupyter Notebook

  3. urban-heat-risk-glasgow-mscurban-heat-risk-glasgow-mscPublic

    Urban heat-risk analysis using LiDAR-derived built form, socioeconomic deprivation, and access to cooling infrastructure · ArcGIS Pro · raster & spatial analysis

  4. eyes-on-the-street-urban-safety-msceyes-on-the-street-urban-safety-mscPublic

    Testing Jane Jacobs' natural surveillance thesis across Glasgow's 746 Data Zones

    HTML

  5. citibike-trip-classification-msccitibike-trip-classification-mscPublic

    Classifying NYC Citi Bike trips as commuter or recreational using spatial and temporal features

    Jupyter Notebook

  6. bus-headway-analysis-manchester-mscbus-headway-analysis-manchester-mscPublic

    Real-time analysis of 310,000+ bus position records along Manchester's Oxford Road corridor

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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  • Glasgow

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fmorenog/README.md
Fabián Moreno Garza
Spatial Data Science · Urban Analytics · GIS
MSc Urban Analytics, University of Glasgow · BArch, Tecnológico de Monterrey
I use spatial data, Python and GIS to study urban and regional problems. My recent work has examined neighbourhood deprivation change, public transport reliability, urban environmental conditions and travel behaviour, using methods ranging from spatial statistics and GIS to machine learning.
Before specialising in urban analytics, I worked in data consultancy and business systems across retail, healthcare, manufacturing and environmental engineering.
What I work with
Spatial & GIS: ArcGIS Pro · GeoPandas · PySAL · Shapely · QGIS (developing)
Python & Data: Python · Pandas · scikit-learn · DuckDB · SQL
Visualisation: Tableau · Power BI
Workflow: Git · GitHub
Featured projects
🏙️ Predicting Neighbourhood Deprivation Change in Glasgow
Investigating whether neighbourhood deprivation change across Glasgow's 746 data zones can be predicted from socioeconomic, housing and built-environment characteristics. The project uses spatial statistics, machine learning and geographically separated model validation to examine how well predictive models generalise across the city.
🚌 Bus Headway & Reliability Analysis — Manchester
Analysis of approximately 310,000 GTFS-Realtime vehicle-position records along Manchester's Oxford Road/Wilmslow Road corridor, examining how bus performance and reliability vary spatially along the route.
🌫️ PM2.5 Prediction with Graph Neural Networks — London
Graph-based modelling of PM2.5 air pollution across 25,053 London Output Areas using GraphSAGE, PyTorch Geometric and a KNN spatial graph constructed from neighbourhood relationships and census characteristics.
Get in touch
📧 fabian.morenog@gmail.com
💼 LinkedIn
🌐 Portfolio

Pinned
Loading
  1. Fabian-MorenoFabian-MorenoPublic
    
About me

  2. glasgow-neighbourhood-decline-mscglasgow-neighbourhood-decline-mscPublic
    
Predicting neighbourhood deprivation change across Glasgow

    Jupyter Notebook
  3. urban-heat-risk-glasgow-mscurban-heat-risk-glasgow-mscPublic
    
Urban heat-risk analysis using LiDAR-derived built form, socioeconomic deprivation, and access to cooling infrastructure · ArcGIS Pro · raster & spatial analysis

  4. eyes-on-the-street-urban-safety-msceyes-on-the-street-urban-safety-mscPublic
    
Testing Jane Jacobs' natural surveillance thesis across Glasgow's 746 Data Zones

    HTML
  5. citibike-trip-classification-msccitibike-trip-classification-mscPublic
    
Classifying NYC Citi Bike trips as commuter or recreational using spatial and temporal features

    Jupyter Notebook
  6. bus-headway-analysis-manchester-mscbus-headway-analysis-manchester-mscPublic
    
Real-time analysis of 310,000+ bus position records along Manchester's Oxford Road corridor


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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 · 1
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  • Glasgow

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fmorenog/README.md
Fabián Moreno Garza
Spatial Data Science · Urban Analytics · GIS
MSc Urban Analytics, University of Glasgow · BArch, Tecnológico de Monterrey
I use spatial data, Python and GIS to study urban and regional problems. My recent work has examined neighbourhood deprivation change, public transport reliability, urban environmental conditions and travel behaviour, using methods ranging from spatial statistics and GIS to machine learning.
Before specialising in urban analytics, I worked in data consultancy and business systems across retail, healthcare, manufacturing and environmental engineering.
What I work with
Spatial & GIS: ArcGIS Pro · GeoPandas · PySAL · Shapely · QGIS (developing)
Python & Data: Python · Pandas · scikit-learn · DuckDB · SQL
Visualisation: Tableau · Power BI
Workflow: Git · GitHub
Featured projects
🏙️ Predicting Neighbourhood Deprivation Change in Glasgow
Investigating whether neighbourhood deprivation change across Glasgow's 746 data zones can be predicted from socioeconomic, housing and built-environment characteristics. The project uses spatial statistics, machine learning and geographically separated model validation to examine how well predictive models generalise across the city.
🚌 Bus Headway & Reliability Analysis — Manchester
Analysis of approximately 310,000 GTFS-Realtime vehicle-position records along Manchester's Oxford Road/Wilmslow Road corridor, examining how bus performance and reliability vary spatially along the route.
🌫️ PM2.5 Prediction with Graph Neural Networks — London
Graph-based modelling of PM2.5 air pollution across 25,053 London Output Areas using GraphSAGE, PyTorch Geometric and a KNN spatial graph constructed from neighbourhood relationships and census characteristics.
Get in touch
📧 fabian.morenog@gmail.com
💼 LinkedIn
🌐 Portfolio

Pinned
Loading
  1. Fabian-MorenoFabian-MorenoPublic
    
About me

  2. glasgow-neighbourhood-decline-mscglasgow-neighbourhood-decline-mscPublic
    
Predicting neighbourhood deprivation change across Glasgow

    Jupyter Notebook
  3. urban-heat-risk-glasgow-mscurban-heat-risk-glasgow-mscPublic
    
Urban heat-risk analysis using LiDAR-derived built form, socioeconomic deprivation, and access to cooling infrastructure · ArcGIS Pro · raster & spatial analysis

  4. eyes-on-the-street-urban-safety-msceyes-on-the-street-urban-safety-mscPublic
    
Testing Jane Jacobs' natural surveillance thesis across Glasgow's 746 Data Zones

    HTML
  5. citibike-trip-classification-msccitibike-trip-classification-mscPublic
    
Classifying NYC Citi Bike trips as commuter or recreational using spatial and temporal features

    Jupyter Notebook
  6. bus-headway-analysis-manchester-mscbus-headway-analysis-manchester-mscPublic
    
Real-time analysis of 310,000+ bus position records along Manchester's Oxford Road corridor


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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0
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 · 1
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  • Glasgow

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fmorenog/README.md
Fabián Moreno Garza
Spatial Data Science · Urban Analytics · GIS
MSc Urban Analytics, University of Glasgow · BArch, Tecnológico de Monterrey
I use spatial data, Python and GIS to study urban and regional problems. My recent work has examined neighbourhood deprivation change, public transport reliability, urban environmental conditions and travel behaviour, using methods ranging from spatial statistics and GIS to machine learning.
Before specialising in urban analytics, I worked in data consultancy and business systems across retail, healthcare, manufacturing and environmental engineering.
What I work with
Spatial & GIS: ArcGIS Pro · GeoPandas · PySAL · Shapely · QGIS (developing)
Python & Data: Python · Pandas · scikit-learn · DuckDB · SQL
Visualisation: Tableau · Power BI
Workflow: Git · GitHub
Featured projects
🏙️ Predicting Neighbourhood Deprivation Change in Glasgow
Investigating whether neighbourhood deprivation change across Glasgow's 746 data zones can be predicted from socioeconomic, housing and built-environment characteristics. The project uses spatial statistics, machine learning and geographically separated model validation to examine how well predictive models generalise across the city.
🚌 Bus Headway & Reliability Analysis — Manchester
Analysis of approximately 310,000 GTFS-Realtime vehicle-position records along Manchester's Oxford Road/Wilmslow Road corridor, examining how bus performance and reliability vary spatially along the route.
🌫️ PM2.5 Prediction with Graph Neural Networks — London
Graph-based modelling of PM2.5 air pollution across 25,053 London Output Areas using GraphSAGE, PyTorch Geometric and a KNN spatial graph constructed from neighbourhood relationships and census characteristics.
Get in touch
📧 fabian.morenog@gmail.com
💼 LinkedIn
🌐 Portfolio

Pinned
Loading
  1. Fabian-MorenoFabian-MorenoPublic
    
About me

  2. glasgow-neighbourhood-decline-mscglasgow-neighbourhood-decline-mscPublic
    
Predicting neighbourhood deprivation change across Glasgow

    Jupyter Notebook
  3. urban-heat-risk-glasgow-mscurban-heat-risk-glasgow-mscPublic
    
Urban heat-risk analysis using LiDAR-derived built form, socioeconomic deprivation, and access to cooling infrastructure · ArcGIS Pro · raster & spatial analysis

  4. eyes-on-the-street-urban-safety-msceyes-on-the-street-urban-safety-mscPublic
    
Testing Jane Jacobs' natural surveillance thesis across Glasgow's 746 Data Zones

    HTML
  5. citibike-trip-classification-msccitibike-trip-classification-mscPublic
    
Classifying NYC Citi Bike trips as commuter or recreational using spatial and temporal features

    Jupyter Notebook
  6. bus-headway-analysis-manchester-mscbus-headway-analysis-manchester-mscPublic
    
Real-time analysis of 310,000+ bus position records along Manchester's Oxford Road corridor


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
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 · 1
following

  • Glasgow

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fmorenog/README.md
Fabián Moreno Garza
Spatial Data Science · Urban Analytics · GIS
MSc Urban Analytics, University of Glasgow · BArch, Tecnológico de Monterrey
I use spatial data, Python and GIS to study urban and regional problems. My recent work has examined neighbourhood deprivation change, public transport reliability, urban environmental conditions and travel behaviour, using methods ranging from spatial statistics and GIS to machine learning.
Before specialising in urban analytics, I worked in data consultancy and business systems across retail, healthcare, manufacturing and environmental engineering.
What I work with
Spatial & GIS: ArcGIS Pro · GeoPandas · PySAL · Shapely · QGIS (developing)
Python & Data: Python · Pandas · scikit-learn · DuckDB · SQL
Visualisation: Tableau · Power BI
Workflow: Git · GitHub
Featured projects
🏙️ Predicting Neighbourhood Deprivation Change in Glasgow
Investigating whether neighbourhood deprivation change across Glasgow's 746 data zones can be predicted from socioeconomic, housing and built-environment characteristics. The project uses spatial statistics, machine learning and geographically separated model validation to examine how well predictive models generalise across the city.
🚌 Bus Headway & Reliability Analysis — Manchester
Analysis of approximately 310,000 GTFS-Realtime vehicle-position records along Manchester's Oxford Road/Wilmslow Road corridor, examining how bus performance and reliability vary spatially along the route.
🌫️ PM2.5 Prediction with Graph Neural Networks — London
Graph-based modelling of PM2.5 air pollution across 25,053 London Output Areas using GraphSAGE, PyTorch Geometric and a KNN spatial graph constructed from neighbourhood relationships and census characteristics.
Get in touch
📧 fabian.morenog@gmail.com
💼 LinkedIn
🌐 Portfolio

Pinned
Loading
  1. Fabian-MorenoFabian-MorenoPublic
    
About me

  2. glasgow-neighbourhood-decline-mscglasgow-neighbourhood-decline-mscPublic
    
Predicting neighbourhood deprivation change across Glasgow

    Jupyter Notebook
  3. urban-heat-risk-glasgow-mscurban-heat-risk-glasgow-mscPublic
    
Urban heat-risk analysis using LiDAR-derived built form, socioeconomic deprivation, and access to cooling infrastructure · ArcGIS Pro · raster & spatial analysis

  4. eyes-on-the-street-urban-safety-msceyes-on-the-street-urban-safety-mscPublic
    
Testing Jane Jacobs' natural surveillance thesis across Glasgow's 746 Data Zones

    HTML
  5. citibike-trip-classification-msccitibike-trip-classification-mscPublic
    
Classifying NYC Citi Bike trips as commuter or recreational using spatial and temporal features

    Jupyter Notebook
  6. bus-headway-analysis-manchester-mscbus-headway-analysis-manchester-mscPublic
    
Real-time analysis of 310,000+ bus position records along Manchester's Oxford Road corridor


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
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0
followers
 · 1
following

  • Glasgow

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fmorenog/README.md
Fabián Moreno Garza
Spatial Data Science · Urban Analytics · GIS
MSc Urban Analytics, University of Glasgow · BArch, Tecnológico de Monterrey
I use spatial data, Python and GIS to study urban and regional problems. My recent work has examined neighbourhood deprivation change, public transport reliability, urban environmental conditions and travel behaviour, using methods ranging from spatial statistics and GIS to machine learning.
Before specialising in urban analytics, I worked in data consultancy and business systems across retail, healthcare, manufacturing and environmental engineering.
What I work with
Spatial & GIS: ArcGIS Pro · GeoPandas · PySAL · Shapely · QGIS (developing)
Python & Data: Python · Pandas · scikit-learn · DuckDB · SQL
Visualisation: Tableau · Power BI
Workflow: Git · GitHub
Featured projects
🏙️ Predicting Neighbourhood Deprivation Change in Glasgow
Investigating whether neighbourhood deprivation change across Glasgow's 746 data zones can be predicted from socioeconomic, housing and built-environment characteristics. The project uses spatial statistics, machine learning and geographically separated model validation to examine how well predictive models generalise across the city.
🚌 Bus Headway & Reliability Analysis — Manchester
Analysis of approximately 310,000 GTFS-Realtime vehicle-position records along Manchester's Oxford Road/Wilmslow Road corridor, examining how bus performance and reliability vary spatially along the route.
🌫️ PM2.5 Prediction with Graph Neural Networks — London
Graph-based modelling of PM2.5 air pollution across 25,053 London Output Areas using GraphSAGE, PyTorch Geometric and a KNN spatial graph constructed from neighbourhood relationships and census characteristics.
Get in touch
📧 fabian.morenog@gmail.com
💼 LinkedIn
🌐 Portfolio

Pinned
Loading
  1. Fabian-MorenoFabian-MorenoPublic
    
About me

  2. glasgow-neighbourhood-decline-mscglasgow-neighbourhood-decline-mscPublic
    
Predicting neighbourhood deprivation change across Glasgow

    Jupyter Notebook
  3. urban-heat-risk-glasgow-mscurban-heat-risk-glasgow-mscPublic
    
Urban heat-risk analysis using LiDAR-derived built form, socioeconomic deprivation, and access to cooling infrastructure · ArcGIS Pro · raster & spatial analysis

  4. eyes-on-the-street-urban-safety-msceyes-on-the-street-urban-safety-mscPublic
    
Testing Jane Jacobs' natural surveillance thesis across Glasgow's 746 Data Zones

    HTML
  5. citibike-trip-classification-msccitibike-trip-classification-mscPublic
    
Classifying NYC Citi Bike trips as commuter or recreational using spatial and temporal features

    Jupyter Notebook
  6. bus-headway-analysis-manchester-mscbus-headway-analysis-manchester-mscPublic
    
Real-time analysis of 310,000+ bus position records along Manchester's Oxford Road corridor


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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fmorenog/README.md
Fabián Moreno Garza
Spatial Data Science · Urban Analytics · GIS
MSc Urban Analytics, University of Glasgow · BArch, Tecnológico de Monterrey
I use spatial data, Python and GIS to study urban and regional problems. My recent work has examined neighbourhood deprivation change, public transport reliability, urban environmental conditions and travel behaviour, using methods ranging from spatial statistics and GIS to machine learning.
Before specialising in urban analytics, I worked in data consultancy and business systems across retail, healthcare, manufacturing and environmental engineering.
What I work with
Spatial & GIS: ArcGIS Pro · GeoPandas · PySAL · Shapely · QGIS (developing)
Python & Data: Python · Pandas · scikit-learn · DuckDB · SQL
Visualisation: Tableau · Power BI
Workflow: Git · GitHub
Featured projects
🏙️ Predicting Neighbourhood Deprivation Change in Glasgow
Investigating whether neighbourhood deprivation change across Glasgow's 746 data zones can be predicted from socioeconomic, housing and built-environment characteristics. The project uses spatial statistics, machine learning and geographically separated model validation to examine how well predictive models generalise across the city.
🚌 Bus Headway & Reliability Analysis — Manchester
Analysis of approximately 310,000 GTFS-Realtime vehicle-position records along Manchester's Oxford Road/Wilmslow Road corridor, examining how bus performance and reliability vary spatially along the route.
🌫️ PM2.5 Prediction with Graph Neural Networks — London
Graph-based modelling of PM2.5 air pollution across 25,053 London Output Areas using GraphSAGE, PyTorch Geometric and a KNN spatial graph constructed from neighbourhood relationships and census characteristics.
Get in touch
📧 fabian.morenog@gmail.com
💼 LinkedIn
🌐 Portfolio

Pinned
Loading
  1. Fabian-MorenoFabian-MorenoPublic
    
About me

  2. glasgow-neighbourhood-decline-mscglasgow-neighbourhood-decline-mscPublic
    
Predicting neighbourhood deprivation change across Glasgow

    Jupyter Notebook
  3. urban-heat-risk-glasgow-mscurban-heat-risk-glasgow-mscPublic
    
Urban heat-risk analysis using LiDAR-derived built form, socioeconomic deprivation, and access to cooling infrastructure · ArcGIS Pro · raster & spatial analysis

  4. eyes-on-the-street-urban-safety-msceyes-on-the-street-urban-safety-mscPublic
    
Testing Jane Jacobs' natural surveillance thesis across Glasgow's 746 Data Zones

    HTML
  5. citibike-trip-classification-msccitibike-trip-classification-mscPublic
    
Classifying NYC Citi Bike trips as commuter or recreational using spatial and temporal features

    Jupyter Notebook
  6. bus-headway-analysis-manchester-mscbus-headway-analysis-manchester-mscPublic
    
Real-time analysis of 310,000+ bus position records along Manchester's Oxford Road corridor


, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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 · 1
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fmorenog/README.md
Fabián Moreno Garza
Spatial Data Science · Urban Analytics · GIS
MSc Urban Analytics, University of Glasgow · BArch, Tecnológico de Monterrey
I use spatial data, Python and GIS to study urban and regional problems. My recent work has examined neighbourhood deprivation change, public transport reliability, urban environmental conditions and travel behaviour, using methods ranging from spatial statistics and GIS to machine learning.
Before specialising in urban analytics, I worked in data consultancy and business systems across retail, healthcare, manufacturing and environmental engineering.
What I work with
Spatial & GIS: ArcGIS Pro · GeoPandas · PySAL · Shapely · QGIS (developing)
Python & Data: Python · Pandas · scikit-learn · DuckDB · SQL
Visualisation: Tableau · Power BI
Workflow: Git · GitHub
Featured projects
🏙️ Predicting Neighbourhood Deprivation Change in Glasgow
Investigating whether neighbourhood deprivation change across Glasgow's 746 data zones can be predicted from socioeconomic, housing and built-environment characteristics. The project uses spatial statistics, machine learning and geographically separated model validation to examine how well predictive models generalise across the city.
🚌 Bus Headway & Reliability Analysis — Manchester
Analysis of approximately 310,000 GTFS-Realtime vehicle-position records along Manchester's Oxford Road/Wilmslow Road corridor, examining how bus performance and reliability vary spatially along the route.
🌫️ PM2.5 Prediction with Graph Neural Networks — London
Graph-based modelling of PM2.5 air pollution across 25,053 London Output Areas using GraphSAGE, PyTorch Geometric and a KNN spatial graph constructed from neighbourhood relationships and census characteristics.
Get in touch
📧 fabian.morenog@gmail.com
💼 LinkedIn
🌐 Portfolio

Pinned
Loading
  1. Fabian-MorenoFabian-MorenoPublic
    
About me

  2. glasgow-neighbourhood-decline-mscglasgow-neighbourhood-decline-mscPublic
    
Predicting neighbourhood deprivation change across Glasgow

    Jupyter Notebook
  3. urban-heat-risk-glasgow-mscurban-heat-risk-glasgow-mscPublic
    
Urban heat-risk analysis using LiDAR-derived built form, socioeconomic deprivation, and access to cooling infrastructure · ArcGIS Pro · raster & spatial analysis

  4. eyes-on-the-street-urban-safety-msceyes-on-the-street-urban-safety-mscPublic
    
Testing Jane Jacobs' natural surveillance thesis across Glasgow's 746 Data Zones

    HTML
  5. citibike-trip-classification-msccitibike-trip-classification-mscPublic
    
Classifying NYC Citi Bike trips as commuter or recreational using spatial and temporal features

    Jupyter Notebook
  6. bus-headway-analysis-manchester-mscbus-headway-analysis-manchester-mscPublic
    
Real-time analysis of 310,000+ bus position records along Manchester's Oxford Road corridor