New paper: Community‑driven tree planting greens the neighbouring landscape

Josh, Rudy and Hywel have recently published a paper analysing the impact of community-based tree planting on greening the landscape in the Mount Kenya region. You can find the work online.

Land degradation reduces the resilience of both terrestrial systems and the capacity of agricultural communities to adapt to climate change. Sub-Saharan Africa has experience extensive land degradation, this is expected to continue in the future and this will threaten the livelihoods of smallholder farmers.

For this project, we partnered with The International Small group and Tree planting program (TIST) in Kenya. TIST is a farmer-led network organised around agroforestry and regenerative farming practices, this network consists of over 100,000 members in Tanzania, Uganda, Kenya and India.

We combine Landsat-7 satellite imagery with site data from TIST farms to examine the effect of TIST tree planting in the Mount Kenya region. We identify a positive greening effect in TIST groves between 2000-2019 relative to the wider agricultural landscape. These groves cover 27,198 ha, and a further 27,750 ha of neighbouring agricultural land is also positively influenced by TIST. TIST also benefits local forests, e.g. through reducing fuelwood and fodder extraction. Our results show that community‑led initiatives can lead to successful landscape‑scale regreening on decadal timescales.

New paper: Community evolution on Stack Overflow

Iraklis and Hywel have recently published a new paper investigating the growth of the Q&A communities on Stack Overflow, which is now available online.

Question and answer (Q&A) websites are a medium where people can communicate and help each other. Stack Overflow is one of the most popular Q&A websites about programming, where millions of developers seek help or provide valuable assistance. Activity on the Stack Overflow website is moderated by the user community, utilizing a voting system to promote high quality content. The website was created on 2008 and has accumulated a large amount of crowd wisdom about the software development industry. Here we analyse this data to examine trends in the grouping of technologies and their users into different sub-communities. In our work we analysed all questions, answers, votes and tags from 21 Stack Overflow between 2008 and 2020. We generated a series of user-technology interaction graphs and applied community detection algorithms to identify the biggest user communities for each year, to examine which technologies those communities incorporate, how they are interconnected and how they evolve through time. The biggest and most persistent communities were related to web development. In general, there is little movement between communities; users tend to either stay within the same community or not acquire any score at all. Community evolution reveals the popularity of different programming languages and frameworks on Stack Overflow over time. These findings give insight into the user community on Stack Overflow and reveal long-term trends on the software development industry.

New paper: Social Sensing of Heatwaves

James, Rudy, Michelle and Hywel have recently published a paper investigating the use of Twitter data for detecting heatwaves. The work is available online.

Heatwaves cause thousands of deaths every year, yet the social impacts of heat are poorly measured. Temperature alone is not sufficient to measure impacts and “heatwaves” are defined differently in different cities/countries. This study used data from the microblogging platform Twitter to detect different scales of response and varying attitudes to heatwaves within the United Kingdom (UK), the United States of America (US) and Australia. At the country scale, the volume of heat-related Twitter activity increased exponentially as temperature increased. The initial social reaction differed between countries, with a larger response to heatwaves elicited from the UK than from Australia, despite the comparatively milder conditions in the UK. Language analysis reveals that the UK user population typically responds with concern for individual wellbeing and discomfort, whereas Australian and US users typically focus on the environmental consequences. At the city scale, differing responses are seen in London, Sydney and New York on governmentally defined heatwave days; sentiment changes predictably in London and New York over a 24-h period, while sentiment is more constant in Sydney. This study shows that social media data can provide robust observations of public response to heat, suggesting that social sensing of heatwaves might be useful for preparedness and mitigation.

New paper: Crowd-sourced observations for short-range numerical weather prediction: Report from EWGLAM/SRNWP Meeting 2019

Hywel and his collaborators have recently published a paper looking at how crowd-sourced data can be used for weather forecasting. The work is available online.

Crowd-sourced observations (CSO) offer great potential for numerical weather prediction (NWP). This paper offers a synthesis of progress, challenges and opportunities in this area based on a special session of the EWGLAM Meeting in 2019, concentrating on high-resolution limited-area models (LAMs). Two main application areas of CSO are described: data assimilation and verification. One part of data assimilation developments concentrates on smartphone pressure observations, which represent a large volume of data. However, special care has to be taken about data protection and the quality of observations. In this paper, two examples are presented: the SMAPS experiment from Denmark and the uWx experiment from the United States. Another data assimilation topic is citizen observations with low-cost weather sensors; here an example from Norway is presented using Netatmo stations. The other application area is the use of CSO for model verification. One novel method developed in the United Kingdom is applying social media data to detect severe weather events. This approach is especially important because one future application area of LAM NWP models is impact-oriented warnings.

New paper: Studying the UK job market during the COVID-19 crisis with online job ads

Rudy has recently published a paper exploring how the COVID-19 restrictions have affected the UK job market. The paper is available online.

The COVID-19 global pandemic and the lockdown policies enacted to mitigate it have had profound effects on the labour market. Understanding these effects requires us to obtain and analyse data in as close to real time as possible, especially as rules change rapidly and local lockdowns are enacted. This work studies the UK labour market by analysing data from the online job board, using topic modelling and geo-inference methods to break down the data by sector and geography. I also study how the salary, contract type, and mode of work have changed since the COVID-19 crisis hit the UK in March. Overall, vacancies were down by 60 to 70% in the first weeks of lockdown. By the end of the year numbers had recovered somewhat, but the total job ad deficit is measured to be over 40%. Broken down by sector, vacancies for hospitality and graduate jobs are greatly reduced, while there were more care work and nursing vacancies during lockdown. Differences by geography are less significant than between sectors, though there is some indication that local lockdowns stall recovery and less badly hit areas may have experienced a smaller reduction in vacancies. There are also small but significant changes in the salary distribution and number of full time and permanent jobs. As well as the analysis, this work presents an open methodology that enables a rapid and detailed survey of the job market in unsettled conditions and describes a web application that allows others to query this data set.