Impact, Diffusion and Scaling-Up of a Comprehensive Land-Use Planning Approach in the Philippines: From Development Cooperation to National Policies

This evaluation report investigates the impact of ten years of comprehensive land-use planning in the Philippines. Characterized by fundamental developmental challenges associated with scarce land resources, environmental degradation, natural hazards and persistent poverty, land-use planning plays a crucial role in finding answers to these pressing challenges. The impact evaluation assesses a technical approach to enhanced land-use planning and capacity development from community to national level, supporting decentralized planning, natural resource governance, and resilience to natural hazards and climate change. The so-called SIMPLE (Sustainable Integrated Management and Planning for Local Government Ecosystems) approach by the Philippine-German cooperation, managed by the Deutsche Gesellschaft für internationale Zusammenarbeit (GIZ), was implemented in two regions of the Visayas. The ambitious intervention operated in a challenging environment with multiple stakeholders, overlapping mandates, and imprecise legal frameworks. In cooperation with GIZ, the Housing and Land Use Regulatory Board (HLURB) rolled out the related enhanced Comprehensive Land Use Planning (eCLUP) guidelines nationwide. Based on a mixed-methods and quasi-experimental design, the evaluation generates relevant findings for the improvement of land-use planning and local governance interventions, for sustainable natural resource management, disaster risk management, and for welfare improvements of communities and beneficiaries. It shows relevant factors for the successful implementation. The report draws important lessons for local planning and the national framework, and suggests solutions to the fundamental gap between planning and plan implementation, improved innovation diffusion and efficient processes, effective community participation, and public accountability

Donor-Assisted Land-use Planning in the Philippines: Insights from a Multi-Level Survey

Land requires fair and transparent management to allow for equal participation and for its sustainable use among rivaling stakeholders. Land use planning is the mechanism to allow for this kind of resource management and the reconciliation of diverging interests. It is thus not surprising that the governance of land resources has become a prominent topic among donors and development practitioners in the last decade. It is theorized that good administration and management of land is crucial to poverty reduction, conflict transformation, disaster risk management, improvement in the quality of local governance and ultimately sustainable economic growth. The report at hand presents first results derived from a quantitative impact evaluation of an intervention for enhanced land use planning in the Philippines. The SIMPLE (Sustainable Integrated Management and Planning for Local Government Ecosystems) approach embedded in the Philippine-German cooperation’s “Environment and Rural Development (EnRD)” program was implemented between 2006 and 2015, managed by the Gesellschaft für internationale Zusammenarbeit (GIZ). The report draws upon quantitative cross-sectional data collected in 2012 on household, village and municipal level. It provides first insights into program outcomes and impacts. A follow-up impact evaluation of the intervention, based on a rigorous before-after design, will be published in 2017

Improving International Development Evaluation through Geospatial Data and Analysis

Increasing availability of new types of data strengthens geospatial research in different scientific fields and opens up opportunities to better measure results and evaluate the impacts of development interventions. This article presents examples where geospatial approaches have been applied in evaluations and thus demonstrate the potential use in informing policy design through scientifically sound evidence as well as learning. The authors illustrate innovative ways of employing geospatial data and analysis in impact evaluations of international development cooperation. These interventions are concerned with topics such as biodiversity conservation, land degradation, sustainable use of natural resources, and disaster risk management. Recent methodological developments in the field of remote sensing and machine learning show significant potential to transform the vast body of new data into meaningful evidence aimed to improve policy and program design. The application and potential of methods are discussed in light of increasing importance of concerns over global climate change and climate change adaptation. The authors call for enhancing mutual interaction between the geospatial research disciplines and the development evaluation community to jointly contribute to finding solutions for tackling pressing social and environmental challenges

A Proof-of-Concept of Integrating Machine Learning, Remote Sensing, and Survey Data in Evaluations: The Measurement of Disaster Resilience in the Philippines

Disaster resilience is a topic of increasing importance for policy makers in the context of climate change. However, measuring disaster resilience remains a challenge as it requires information on both the physical environment and socio-economic dimensions. In this study we developed and tested a method to use remote sensing (RS) data to construct proxy indicators of socio-economic change. We employed machine-learning algorithms to generate land-cover and land-use classifications from very high-resolution satellite imagery to appraise disaster damage and recovery processes in the Philippines following the devastation of typhoon Haiyan in November 2013. We constructed RS-based proxy indicators for N=20 barangays (villages) in the region surrounding Tacloban City in the central east of the Philippines. We then combined the RS-based proxy indicators with detailed socio-economic information collected during a rigorous-impact evaluation by DEval in 2016. Results from a statistical analysis demonstrated that fastest post-disaster recovery occurred in urban barangays that received sufficient government support (subsidies), and which had no prior disaster experience. In general, socio-demographic factors had stronger effects on the early recovery phase (0-2 years) compared to the late recovery phase (2-3 years). German development support was related to recovery performance only to some extent. Rather than providing an in-depth statistical analysis, this study is intended as a proof-of-concept. We have been able to demonstrate that high-resolution RS data and machine-learning techniques can be used within a mixed-methods design as an effective tool to evaluate disaster impacts and recovery processes. While RS data have distinct limitations (e.g., cost, labour intensity), they offer unique opportunities to objectively measure physical, and by extension socio-economic, changes over large areas and long time-scales.Zunehmende Wetterextreme und Naturkatastrophen sind Folgen des Klimawandels. Aufgrund dieser steigenden Risiken rückt die Resilienz der Bevölkerung im Katastrophenfall als zentrales Thema in den Vordergrund und hat zunehmende Bedeutung für politische Entscheidungstragende. Dennoch bleibt die Messung des mehrdimensionalen Konzepts der Katastrophenresilienz eine Herausforderung, da sie Informationen sowohl über die physische Umgebung als auch sozioökonomische Faktoren erfordert. In dieser Studie wird eine Methode entwickelt, um aus Fernerkundungsdaten (RS-Daten) Indikatoren zu entwickeln, die Aspekte des sozioökonomischen Wandels approximieren und somit messbar machen (Proxy-Indikatoren). Zu diesem Zweck wurden Algorithmen des maschinellen Lernens eingesetzt. Mit Hilfe dieser Algorithmen wurden aus hochauflösenden Satellitenbildern Klassifizierungen für Landstruktur und Landnutzung konstruiert, um Katastrophenschäden und iederaufbauprozesse auf den Philippinen nach der Zerstörung durch den Taifun Haiyan im November 2013 zu messen. Aus den RS-Daten wurden die Indikatoren für N=20 Barangays (Dörfer) in der Region um die Stadt Tacloban im zentralen Osten der Philippinen berechnet. Diese auf RS-Daten basierenden Indikatoren wurden mit detaillierten sozioökonomischen Informationen kombiniert, die für eine DEval-Evaluierung im Jahr 2016 erhoben wurden. Die Ergebnisse der statistischen Analyse zeigen, dass der schnellste Wiederaufbau nach der Katastrophe in städtischen Barangays zu beobachten war, die ausreichend staatliche Unterstützung (Subventionen) erhielten und über keine Katastrophenerfahrung verfügten. Im Vergleich hatten soziodemografische Faktoren allgemein stärkere Auswirkungen auf die frühe (0-2 Jahre) als auf die spätere (2-3 Jahre) Wiederaufbauphase. Es konnte nur ein bedingter Bezug zwischen der deutschen Entwicklungszusammenarbeit und den Wiederaufbauerfolgen festgestellt werden. Diese Studie versteht sich als Nachweis der Machbarkeit, weniger als detaillierte statistische Analyse. Sie belegt, dass hochauflösende RS-Daten und Techniken des maschinellen Lernens innerhalb eines integrierten Methodendesigns als effektives Werkzeug zur Bewertung von Katastrophenauswirkungen und Wiederherstellungsprozessen eingesetzt werden können. Trotz spezifischer Einschränkungen (hohe Kosten, Arbeitsintensität etc.) bieten RS-Daten einzigartige Möglichkeiten sowohl Umweltbedingungen als auch sozioökonomische Veränderungen über große Gebiete und lange Zeiträume hinweg objektiv messen zu können

Evaluierung aus der Vogelperspektive: Innovativer Einsatz von Fernerkundungstechniken

Eine systematische Nutzung von RS-Daten ermöglicht durch Hinzunahme einer räumlichen Dimension eine bessere Beantwortung der Evaluierungsfragen. In diesem Policy Brief wird der methodische Ansatz von DEval zur Analyse von hochauflösenden RS-Daten unter Anwendung von Bildklassifizierungstechniken und Techniken des maschinellen Lernens (ML) vorgestellt. DEval hat den Ansatz in enger Zusammenarbeit mit RS-Fachleuten der Fakultät für Geoinformationswissenschaften und Erdbeobachtung (ITC) an der Universität Twente in den Niederlanden entwickelt

Evaluation from the Bird's-Eye View: Innovative Use of Remote Sensing Techniques

A systematic use of Remote Sensing (RS) data opens a door for evaluators to better address evaluation questions by adding a spatial dimension. This policy brief highlights DEval’s methodological approach to the analysis of high-resolution RS data through the application of image classification and machinelearning (ML) techniques. DEval has been developing this approach in close cooperation with RS experts from the Faculty of Geo-information Science and Earth Observation (ITC) at the University of Twente in the Netherlands

Evaluating Resilience-Centered Development Interventions with Remote Sensing

Natural disasters are projected to increase in number and severity, in part due to climate change. At the same time a growing number of disaster risk reduction (DRR) and climate change adaptation measures are being implemented by governmental and non-governmental organizations, and substantial post-disaster donations are frequently pledged. At the same time there has been increasing demand for transparency and accountability, and thus evidence of those measures having a positive effect. We hypothesized that resilience-enhancing interventions should result in less damage during a hazard event, or at least quicker recovery. In this study we assessed recovery over a 3 year period of seven municipalities in the central Philippines devastated by Typhoon Haiyan in 2013. We used very high resolution optical images (<1 m), and created detailed land cover and land use maps for four epochs before and after the event, using a machine learning approach with extreme gradient boosting. The spatially and temporally highly variable recovery maps were then statistically related to detailed questionnaire data acquired by DEval in 2012 and 2016, whose principal aim was to assess the impact of a 10 year land-planning intervention program by the German agency for technical cooperation (GIZ). The survey data allowed very detailed insights into DRR-related perspectives, motivations and drivers of the affected population. To some extent they also helped to overcome the principal limitation of remote sensing, which can effectively describe but not explain the reasons for differential recovery. However, while a number of causal links between intervention parameters and reconstruction was found, the common notion that a resilient community should recover better and more quickly could not be confirmed. The study also revealed a number of methodological limitations, such as the high cost for commercial image data not matching the spatially extensive but also detailed scale of field evaluations, the remote sensing analysis likely overestimating damage and thus providing incorrect recovery metrics, and image data catalogues especially for more remote communities often being incomplete. Nevertheless, the study provides a valuable proof of concept for the synergies resulting from an integration of socio-economic survey data and remote sensing imagery for recovery assessment

A proof-of-concept of integrating machine learning, remote sensing, and survey data in evaluations: The measurement of disaster resilience in the Philippines

Disaster resilience is a topic of increasing importance for policy makers in the context of climate change.However, measuring disaster resilience remains a challenge as it requires information on both the physical environment and socio-economic dimensions. In this study we developed and tested a method to use remote sensing (RS) data to construct proxy indicators of socio-economic change. We employed machine-learning algorithms to generate land-cover and land-use classifications from very high-resolution satellite imagery to appraise disaster damage and recovery processes in the Philippines following the devastation of typhoon Haiyan in November 2013. We constructed RS-based proxy indicators for N=20 barangays (villages) in the region surrounding Tacloban City in the central east of the Philippines. We then combined the RS-based proxy indicators with detailed socio-economic information collected during a rigorous-impact evaluation by DEval in 2016. Results from a statistical analysis demonstrated that fastest post-disaster recovery occurred in urban barangays that received sufficient government support (subsidies), and which had no prior disaster experience. In general, socio-demographic factors had stronger effects on the early recovery phase (0–2 years) compared to the late recovery phase (2–3 years). German development support was related to recovery performance only to some extent. Rather than providing an in-depth statistical analysis, this study is intended as a proof-of -concept. We have been able to demonstrate that high-resolution RS data and machine-learning techniques can be used within a mixed-methods design as an effective tool to evaluate disaster impacts and recovery processes. While RS data have distinct limitations (e.g., cost, labour intensity), they offer unique opportunities to objectively measure physical, and by extension socio-economic, changes over large areas and long time-scales