Month: March 2025

Digitally-Enabled Research on Remote Rural Areas: Are We Losing More Than We Gain?

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~ Richard Heeks ~ Leave a comment

In an era where digital technologies shape how we understand the world, research on upland regions—hills, moors, plateaus, and mountains—is increasingly reliant on digital datasets, techniques, and tools. From GIS mapping to AI-driven modelling, these advancements promise deeper insights into the complexities of these remote rural areas. But at what cost?

My new paper, Digitally-Enabled Uplands Research: Review, Critique and Future Principles for Use of Digital in Research on Hill and Mountain Regions, critically examines how digital research is transforming our approach to uplands, and the unintended consequences that come with it.

The Digital Takeover of Uplands Research

The rise of digital technology in uplands research has created efficiencies in data collection, analysis, and visualisation. Researchers now use earth observation data, drone-mounted cameras, and AI-powered models to measure erosion, predict biodiversity changes, and monitor land use with unprecedented precision.

However, this digital shift comes with challenges. Upland issues are increasingly being framed in purely technical terms, sidelining social, cultural, and political dimensions. Digital technologies provide powerful tools, but they also shape what we consider worth measuring—and what remains invisible.

The Risks of a Data-Driven View

One key critique of the paper is the rendering technical of uplands. Research is often structured around technological solutions, meaning problems are defined in a way that makes them solvable through digital methods. While this might improve efficiency, it can also exclude the complexity of upland life, from the lived experiences of farmers to the role of local knowledge in managing landscapes.

Additionally, the scientific datafication of uplands often reduces them to a collection of data points. Remote sensing can generate stunningly detailed maps, but it may strip away the depth and nuance that come with human engagement. The paper highlights how researchers make decisions about what to measure—often prioritizing readily-quantifiable aspects like soil composition or vegetation cover while ignoring human perspectives.

Who Benefits from Digital Uplands Research?

A major concern is adverse digital incorporation—a process where data is extracted from uplands primarily for the benefit of researchers, while local stakeholders remain uninvolved. The study finds that data is rarely shared with upland communities, and research processes often exclude them entirely. This leads to an imbalance: uplands become sites of knowledge extraction rather than empowered participants in digital research.

A Call for More Inclusive Research

The paper concludes with a call for researchers to adopt a more responsible and inclusive approach. It proposes a checklist of principles that encourage:

✔ Acknowledging the social and political realities of uplands

✔ Engaging with local communities as knowledge partners

✔ Making research outputs accessible and usable beyond academia

As digital tools continue to shape uplands research, it’s crucial to reflect on whether they are serving the regions they study—or merely serving academic interests.

📖 Read the full paper here: https://research.manchester.ac.uk/en/publications/digitally-enabled-uplands-research-review-critique-and-future-pri

A first draft of this post was created using ChatGPT. Image: https://www.vecteezy.com/photo/5296281-flying-drone-with-camera-on-the-mountain

Bridging the Digital Divide in Mountain Regions: Lessons from Peru

~ Richard Heeks ~ Leave a comment

Mountain communities are among the most remote and excluded populations in the world. Physical barriers limit access to education, healthcare, and economic opportunities, reinforcing cycles of poverty. But can digital technology help bridge this divide? A study by myself and Laura León Kanashiro explores this question by analysing the impact of telecentres in Pazos, a remote district in the Peruvian Andes.

Our research, published in the Digital Development Working Paper Series, provides valuable insights into how ICTs can enhance connectivity, access to knowledge, and local development—while also exposing the challenges that limit their effectiveness.

Telecentres: A Digital Lifeline?

The study focuses on the ERTIC telecentre project, which aimed to provide rural Peruvians with access to computers, the internet, and digital training. The findings reveal a mixed picture:

✅ Empowering Young Users: Teenagers in Pazos used the telecentre primarily for social purposes—maintaining contact with friends and family in distant cities. This digital connectivity helped sustain social networks, reducing feelings of isolation.

✅ Agricultural Knowledge Hub: Young farmers accessed online information on crops, pests, and farming techniques. Some successfully applied new knowledge to improve agricultural yields, demonstrating the potential economic benefits of digital inclusion.

🚧 Barriers to Success: Despite these positive outcomes, many community members struggled to benefit from the telecentre. Limited digital literacy, lack of locally relevant online content, and economic constraints meant that only those with pre-existing advantages—such as education and financial resources—could fully utilise the technology.

The Information Chain Model: More Than Just Connectivity

One of the key takeaways from the study is that simply providing internet access is not enough. Effective use of ICTs in mountain regions requires a full “information chain” (shown below), where technology is combined with:

🔹 Digital skills training – Users need to know how to search for, assess, and apply online information.
🔹 Local content creation – Information should be relevant to the specific challenges of mountain communities.
🔹 Institutional support – Partnerships with agricultural experts, educators, and local governments can amplify the impact of ICT initiatives.

Recommendations for Future ICT Projects

To maximise the benefits of ICTs for remote communities, we suggest several policy and practice recommendations:

🔹 Expand digital literacy programmes to ensure broader participation, especially among women and older community members.
🔹 Develop locally relevant content in native languages, focused on agriculture, trade, and governance.
🔹 Integrate ICTs with economic opportunities, helping farmers connect to wider markets rather than just accessing information.
🔹 Foster collaboration between governments, NGOs, and local institutions to ensure long-term sustainability.

Conclusion: Technology Alone Won’t Solve Digital Exclusion

This study highlights an important lesson: ICTs can help reduce remoteness, but they do not automatically lead to social or economic inclusion. To truly bridge the digital divide, interventions must go beyond infrastructure and focus on skills, support, and relevance.

If you’re interested in digital inclusion, sustainable mountain development, or ICT4D more generally, then you can get further details from the full paper: https://hummedia.manchester.ac.uk/institutes/gdi/publications/workingpapers/di/di_wp38.pdf

What do you think? How can we make ICTs more effective for remote communities? Share your thoughts in the comments!

Assessing Data Inequality: The CRAB Approach

~ Richard Heeks ~ 1 Comment

Data inequality matters, but how do you measure it?  Say you have a dataset in front of you: how would you assess whether and how it contributes to inequality?

I’m going to offer the CRAB approach, arguing for assessment of Control, Representation, Access and Benefit.

Why CRAB?

At the very end of this post, I’ve summarised and interpreted the dimensions of potential inequality arising in some sample papers[i] from three literatures: on data inequality, on data justice, and on data poverty.  The starting point was Jonathan Cinnamon’s paper on data inequalities, which draws out access, representation and control.  While these three are reflected in other literature, the data justice literature particularly also talks about the impact of data use, framed in terms of who benefits from that use.

Drawing from these different sources then, to assess a dataset, the following checklist can be used:

Dimension of Data InequalitySub-DimensionQuestion
ControlOwnershipWho owns the dataset (and who does not)?
Decision powerWho has the power to make decisions about dataset content and use (and who does not)?
Representation(In)visibilityWho is and is not represented in the dataset?
Data qualityAre there differences in the quality of data (completeness, accuracy, etc) between different groups in the dataset?
AccessTheoryWho does and does not have access to the dataset in theory?
PracticeWho is and is not able to use the dataset in practice?
BenefitWinnersWho benefits from use of the dataset?
Non-winnersDoes any group have an interest in the dataset but not benefit from it?
LosersIs any group harmed by use of the dataset?

In each instance, the questions would potentially identify two or more groups between which one form of data inequality had been exacerbated as a result of the existence of the dataset.  While phrased here in terms of groups, other forms of inequality could be encompassed e.g. between locations or between human and non-human entities. Based on the analysis and identified inequalities, an argument can be made for more participative approaches to data gathering, data processing and data use.

A next step will be putting CRAB into practice by applying it to specific datasets.

Finally, the table of interpreted data inequality dimensions from literature:

SourceDimensionsInterpretation
DATA INEQUALITY
Cinnamon, J. (2020). Data inequalities and why they matter for development. Information Technology for Development, 26(2), 214-233.“access to data … data ‘haves’ and ‘have nots’”Access  
“representation of the world as data … partial and biased digital representations”Representation
“control over data flows … the unequal accumulation of personal behavioral data and the inability to control how it flows between stakeholders”Control
Fisher, A., & Streinz, T. (2021). Confronting data inequality. Columbia Journal of Transnational Law, 60, 829-956.“We employ the term “data inequality” to refer to unequal control over data, understood both in distributional terms (i.e., having or not having data) and in terms of the power to datafy (i.e., deciding what becomes or does not become data).”Access
Control
Yang, A., Fan, H., Jia, Q., Ma, M., Zhong, Z., Li, J., & Jing, N. (2024). How do contributions of organizations impact data inequality in OpenStreetMap?. Computers, Environment and Urban Systems, 109, 102077. “completeness of data varies greatly across different regions”Representation
DATA JUSTICE
Taylor, L. (2017). What Is Data Justice? The Case for Connecting Digital Rights and Freedoms on the Global Level. TILT, Tilburg University, Netherlands“Visibility … Access to representation … Informational privacy”Representation  
“Engagement with technology … Sharing in data’s benefits … Autonomy in technology choices”Access
Benefit
Control
“Non-discrimination … the power to identify and challenge bias in data use, and the freedom not to be discriminated against.”Control
Representation
Heeks, R., & Shekhar, S. (2019). Datafication, development and marginalised urban communities: An applied data justice framework. Information, Communication & Society, 22(7), 992-1011.“Procedural: fairness in the way in which data is handled.”Access
“Instrumental: fairness in the results of data being used.”Benefit
“Rights-based: adherence to basic data rights such as representation, privacy, access and ownership.”Representation
Access
Control
Pritchard, R., Sauls, L. A., Oldekop, J. A., Kiwango, W. A., & Brockington, D. (2022). Data justice and biodiversity conservation. Conservation Biology, 36(5), e13919.“data composition … who is seen and how”Representation
“data control, involves asking who funds data collection, determines the content of the data, and has the power to influence how data are shared and used”Control
“Data access … whether data are freely available in digital form … [and whether] everyone [has] the technical capacities and expertise required to benefit”Access
“data processing and use … who uses data, how are different data sets analyzed and combined, and how are results presented in information products”Access
Representation
“data consequences … the choices made based on data, the ways these choices remake the world, and how they alter the resulting distribution of costs and benefits”Benefit
DATA POVERTY
Lucas, P. J., Robinson, R., & Treacy, L. (2020). What is Data Poverty. Nesta, Edinburgh“those individuals, households or communities who cannot afford sufficient, private and secure mobile or broadband data to meet their essential needs”Access
Ibrahim, H., Liu, X., Zariffa, N., Morris, A. D., & Denniston, A. K. (2021). Health data poverty: an assailable barrier to equitable digital health care. The Lancet Digital Health, 3(4), e260-e265.“the inability for individuals, groups, or populations to benefit from a discovery or innovation due to a scarcity of data that are adequately representative”Benefit
Representation
Paik, K. E., Hicklen, R., Kaggwa, F., Puyat, C. V., Nakayama, L. F., Ong, B. A., … & Villanueva, C. (2023). Digital Determinants of Health: Health data poverty amplifies existing health disparities—A scoping review. PLOS Digital Health, 2(10), e0000313.“when certain people groups are underrepresented in generated health data, so that they may actually be harmed by these new tools”Representation
Benefit

[i] Drawn from the top items on Google Scholar with the terms appearing in their titles

Image: adapted from this source