Merge pull request #4 from Urban-Analytics-Technology-Platform/projec…

…t_contnet

Adding the rest of the projects

src/content/project/atip.md

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+---
+name: 'ATIP'
+description: "An online tool to design, modify and share active travel infrastructure plans as part of ATE's offering"
+image:
+    url: '/images/atip.png'
+    alt: 'Screenshot showing the ATIP application'
+tags: ["modelling", "quality of life", "land use"]
+github: "https://github.com/acteng/atip"
+link: "https://acteng.github.io/atip/?style=streets#5.94/53.021/-1.825"
+tier: 1
+---
+
+<iframe style="width:100%;min-height:500px" src="https://acteng.github.io/atip/?style=streets#5.94/53.021/-1.825"/>

src/content/project/bus_spotting.md

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+---
+name: 'Bus Spotting'
+description: 'View GTFS public transit data and compare real-time bus data to idealized schedules'
+image:
+    url: '/images/bus_spotting.png'
+    alt: 'Screenshot showing the bus spotting application'
+tags: ["transport"]
+github: "https://github.com/dabreegster/bus_spotting"
+link: "https://github.com/dabreegster/bus_spotting"
+tier: 1
+---
+

src/content/project/clim_recal.md

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+---
+name: 'Clim Recal'
+description: 'Collection of methods to de-bias climate projection data (sub-component of DyME-CHH but also used as independent codebase)'
+image:
+    url: 'https://www.turing.ac.uk/sites/default/files/styles/hero/public/2021-12/marek-piwnicki-2kqxgrgunzq-unsplash.jpg?itok=FXDUcYO4'
+    alt: 'Image of hills with trees and the glow of a fire behind them'
+tags: ["modelling", "quality of life", "land use"]
+github: "https://github.com/alan-turing-institute/clim-recal"
+link: "https://github.com/alan-turing-institute/clim-recal"
+tier: 2
+---
+
+Regional climate models (RCMs) contain systematic errors, or biases in their output [1]. Biases arise in RCMs for a number of reasons, such as the assumptions in the general circulation models (GCMs), and in the downscaling process from GCM to RCM [1,2].
+
+Researchers, policy-makers and other stakeholders wishing to use publicly available RCMs need to consider a range of "bias correction” methods (sometimes referred to as "bias adjustment" or "recalibration"). Bias correction methods offer a means of adjusting the outputs of RCM in a manner that might better reflect future climate change signals whilst preserving the natural and internal variability of climate [2]. 
+
+The aim of **clim-recal** is therefore to: 
+
+* To provide non-climate scientists with an extensive guide to the application, disadvantages/advantages and use of BC methods 
+* To provide researchers with a collated set of resources for how to technically apply the BC methods, with a framework for open additions 
+* To create accessible information on bias adjustment methods for non quantitative researchers and lay-audience stakeholders 
+
+We are working in partnership with the MetOffice to ensure the propriety, quality, and usability of our work. We're focusing on the UKCP18 suite of products, with the first dataset of focus their ground-breaking local-scale (2.2km) [Convection Permitting Model (CPM)](https://www.metoffice.gov.uk/pub/data/weather/uk/ukcp18/science-reports/UKCP-Convection-permitting-model-projections-report.pdf). 

src/content/project/colouring_cities.md

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+---
+name: 'Colouring Cities/Colouring London'
+description: 'Colouring Cities/Colouring London'
+image:
+    url: 'https://alan-turing-institute.github.io/demoland-web/assets/map-7fd64ded.png'
+    alt: 'Screenshot showing the demoland application'
+tags: ["modelling", "quality of life", "land use"]
+github: "https://github.com/alan-turing-institute/demoland-web"
+link: "https://colouringlondon.org/"
+tier: 2
+---
+
+Colouring London is a free knowledge exchange platform designed to provide over fifty types of open data on buildings in the city, to help make the city more sustainable.
+
+
+<iframe  style="width:100%;min-height:500px" src="https://colouringlondon.org/" title="demoland"/>

src/content/project/dyme.md

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+---
+name: 'DyME - Climate, Heat and Health (DyME-CHH)'
+description: 'Use disaggregated climate data to model the health effects of heat exposure in different population groups, based on where they live and how they move '
+image:
+    url: 'https://www.turing.ac.uk/sites/default/files/styles/hero/public/2021-12/marek-piwnicki-2kqxgrgunzq-unsplash.jpg?itok=FXDUcYO4'
+    alt: 'Image of hills with trees and the glow of a fire behind them'
+tags: ["modelling", "climate"]
+github: "https://www.turing.ac.uk/research/research-projects/impacts-climate-change-and-heat-health"
+link: "https://www.turing.ac.uk/research/research-projects/impacts-climate-change-and-heat-health"
+tier: 1
+---
+
+# Introduction
+
+The climate emergency is bringing critical changes to our ability to live healthy and prosperous lives.  A changing climate and increasing temperatures, especially in urban areas, means that, more than ever before, we need to understand the complex relationships between our human behaviour, the environment in which we live, and our health in order to develop effective mitigation strategies.
+
+This project brings together expertise from across the UK to develop the most detailed model to date of how projected future extreme heat events will affect different population groups, and inequalities in those effects, and aid in developing interventions that will, ultimately, save lives. The tools will be developed in concert with local authorities to support their decision-making around strategies to tackle the climate emergency locally.
+
+# Explaining the science
+
+Our interaction with the natural environment plays a crucial role in all aspects of society: our health, wealth, safety, education, and future prosperity. Climate change will bring fundamental changes to our environment – changes that have the potential to pose significant threats to people’s health and wellbeing. The UKCP18 climate projections indicate “a greater chance of warmer, wetter winters and hotter, drier summers” . Increased temperatures will pose a series of challenges related to health and well-being, especially in urban areas where there is the potential for great inequalities in the effects experienced by different people. For example, high density, low income populations in the centres of our cities may be the most affected by hotter summers but the least well-equipped to ventilate their homes due to factors such as air pollution, noise, low quality housing, and the risk of crime with open windows etc.
+
+Estimating the risks associated with higher temperatures in epidemiological studies and exposures used in health impact analyses are almost exclusively based on aggregate measures of heat (e.g. averages of measurements in an urban area or of model outputs) with the assumption that all members of the population experience the same temperatures. In reality, different members of the population will spend different amounts of time in different locations, i.e. outdoors and indoors in different types of building stock.
+
+The ability to produce high quality, disaggregated information on heat exposures experienced by different population groups will provide a step-change in our understanding of the adverse effects associated with higher temperatures. The ability to generate information on the exposures experienced by different population groups will be essential in developing adaptation measures that provide cooler, healthier, temperatures in times of extreme heat in homes and public spaces such as schools.
+
+# Project aims
+
+The aim of this project is to develop models that will provide new information on ‘personal exposures’ to environmental hazards, specifically those related to exposure to future increased temperatures associated with climate change. Based on a microsimulation modelling approach that integrates environmental conditions with human behaviour, the results will feed into health impact analyses and highlight population groups that may be disproportionately at risk and inform local adaptation strategies to climate change risks. The project will build upon the DyME (Dynamic Models for Environments) framework and tools that were created as part of the Royal Society RAMP Urban Analytics programme that accelerated development of technological innovation in the use of dynamic microsimulation models in real-world applications.
+
+Integrating the foundations DyMe: the QUANT and SPENSER platforms for urban mobility and dynamic microsimulation, with the Joint Centre for Excellence in Environmental Intelligence’s Climate Impacts Mitigation Adaptation and Resilience (CLIMAR) framework and existing work on user interfaces in DyME the outputs of the project will include new data products, tools and interfaces for estimating the local effects of climate and heat on health.
+
+# Applications
+
+The project will work with Cornwall County Council to explore one of their key interests related to climate change: the effects of changing temperatures on active travel, damp in homes (with warmer, wetter winters) and other health related outcomes. The outputs from DyME-CHH will feed directly into The Local Climate Adaptation Tool (LCAT) that supports evidence-based adaptation by local authorities and public bodies. LCAT brings together climate information, adaptation options and health impact evidence to help users understand the health implications of climate change in their local area. Importantly, LCAT also generates recommendations of appropriate adaptation approaches, based on the best available evidence that will support the health and wellbeing of local people.
+
+Other examples include feeding into the City of Bradford Metropolitan Council’s use of data analytics to provide information on the impacts of climate change and sustainability, and working with the Department for Education who are particularly interested in the role of schools from a context of climate change and sustainability. The outputs of this work will link with the Connected Bradford dataset to meet their green agenda goals. This will allow a better understanding of the impact of school building design on outcomes for children and young people. In Bristol, the project will link the outputs of ‘personal exposures’ to increased heat for different population groups to housing stock and feed directly into their aims to have its estate climate resilient by 2030.
+

src/content/project/ltn.md

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+---
+name: 'Low Traffic Neighbourhoods tool (LTN)'
+description: 'A software tool and web app to study and communicate about design and impact of LTNs. Dev'
+image:
+    url: 'https://a-b-street.github.io/docs/software/ltn/ltn.gif'
+    alt: 'Screenshot showing the LTN application'
+tags: ["ATL","traffic"]
+github: "https://a-b-street.github.io/docs/software/ltn/index.html"
+link: "https://a-b-street.github.io/docs/software/ltn/index.html"
+tier: 1
+---

src/content/project/od2net.md

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+---
+name: 'od2net'
+description: 'Tool to rapidly generate route networks in different formats'
+tags: ["osm", "od"]
+github: "https://github.com/dabreegster/od2net/"
+link: "https://github.com/dabreegster/od2net/"
+tier: 2
+---

src/content/project/odjitter.md

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+---
+name: 'odjitter'
+description: 'Disaggregate zone-based origin/destination data to specific points for building travel-demand models'
+image:
+    url: 'https://github.com/dabreegster/odjitter/raw/main/README_files/figure-commonmark/thresholddemo-1.png'
+    alt: 'Showing a set of OD pairs which have been processed by the application'
+tags: ["transport","OD"]
+github: "https://github.com/dabreegster/odjitter"
+link: "https://github.com/dabreegster/odjitter"
+tier: 1
+---
+

src/content/project/osm2streets.md

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+---
+name: 'osm2streets'
+description: 'Transform OpenStreetMap data to street networks with detailed geometry and semantics'
+image:
+    url: '/images/osm2streets.png'
+    alt: 'Screenshot showing the osm2streets application'
+tags: ["osm", "data"]
+github: "https://github.com/a-b-street/osm2streets"
+link: "https://osm2streets.org/"
+tier: 2
+---
+
+<iframe  style="width:100%;min-height:500px" src="https://a-b-street.github.io/osm2streets/#1/0/0" title="demoland"/>

src/content/project/route_snapper.md

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+---
+name: 'Route Snapper'
+description: 'Draw routes in MapLibre snapped to a street network using client-side routing'
+image:
+    url: 'https://github.com/dabreegster/route_snapper/raw/main/demo.gif'
+    alt: 'Animation showing someone drawing a route in MapLibre and it snapping to roads'
+tags: ["frontend","streets"]
+github: "https://github.com/dabreegster/route_snapper"
+link: "https://github.com/dabreegster/route_snapper"
+tier: 2
+---

src/content/project/spc.md

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+---
+name: 'Synthetic Population Catalyst (SPC)'
+description: 'A tool to model individual UK people and their activities, combining a variety of data sources and methods. SPC generates rich synthetic populations for the whole of Britain, with demographic, health, activity information.'
+image:
+    url: 'https://github.com/alan-turing-institute/uatk-spc/raw/main/docs/img/SPC_Schema.png'
+    alt: 'Diagram showing the process of generating a sythetic population'
+tags: ["modelling","populations","synthetic"]
+github: "https://github.com/alan-turing-institute/demoland-web"
+link: "https://github.com/alan-turing-institute/uatk-spc"
+tier: 2
+---
+

src/content/project/urban_grammar.md

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+---
+name: 'Urban Grammar (Signatures)'
+description: 'This project uses satellite imagery, Ordnance Survey data and ML models to characterise urban area use into a number of distinct categories'
+image:
+    url: 'https://urbangrammarai.xyz/_static/logo.svg'
+    alt: 'Logo of the urban grammar project'
+tags: ["buildings", "cities", "crowdsourcing"]
+github: "https://github.com/urbangrammarai"
+link: "https://urbangrammarai.xyz/"
+tier: 2
+---
+
+<iframe  style="width:100%;min-height:500px" src="https://urbangrammarai.xyx" title="demoland"/>