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

Parsing Perceptions of Place: Locative and Textual Representations of Place Émilie-Gamelin on Twitter

We increasingly engage in geographies mediated by social media, which is changing how we experience and produce places. This raises questions about how ‘place’ is conceived and received in networked virtual spaces. Place has remained difficult to grasp in both geography and communications studies that utilize social media data. To attend to this, I first develop a conceptual framework that bridges the phenomenology of spatiality with the communication of place. I then present a case study of Place Émilie-Gamelin in Montreal: a plaza located atop the city’s busiest transit hub. Despite its geographic centrality, it is a liminal space appropriated by marginalized groups and contentious political movements. Since 2015, it has been subject to a city-led revitalization program with intentions of attracting party-goers and tourists. Using a communications geography framework, I collected a year’s worth of tweets, first, employing a filter to capture georeferenced tweets in and around the study site, and second, using the site’s toponyms to retrieve tweets through textual queries. To understand these representations, I coded them by relevance, theme and communicative function. Results showed a place evolving in scope, name and meaning, reflecting diverging flows and uses. I found that there were more textual connotations of the study site than there were geotweets, and that the former were more diverse in their representation of place. The thesis demonstrates how promotional content on Twitter should be more critically analyzed in concert with expressive and descriptive tweets and geotweets, and that this implies spatial ontologies and data collection methods that consider a place on social media as a discursive construction. This is especially so since Twitter has become increasingly ‘platial’ through internal changes and its entwinement with other social media platforms: changes which require consideration in all Twitter-based spatial and textual analyses. The study provides an updated perspective on Twitter’s use in the spatial humanities, GIScience and geography and contributes to those interested in applying more nuanced cartographies of places

Parsing Perceptions of Place: Locative and Textual Representations of Place Émilie-Gamelin on Twitter

We increasingly engage in geographies mediated by social media, which is changing how we experience and produce places. This raises questions about how ‘place’ is conceived and received in networked virtual spaces. Place has remained difficult to grasp in both geography and communications studies that utilize social media data. To attend to this, I first develop a conceptual framework that bridges the phenomenology of spatiality with the communication of place. I then present a case study of Place Émilie-Gamelin in Montreal: a plaza located atop the city’s busiest transit hub. Despite its geographic centrality, it is a liminal space appropriated by marginalized groups and contentious political movements. Since 2015, it has been subject to a city-led revitalization program with intentions of attracting party-goers and tourists. Using a communications geography framework, I collected a year’s worth of tweets, first, employing a filter to capture georeferenced tweets in and around the study site, and second, using the site’s toponyms to retrieve tweets through textual queries. To understand these representations, I coded them by relevance, theme and communicative function. Results showed a place evolving in scope, name and meaning, reflecting diverging flows and uses. I found that there were more textual connotations of the study site than there were geotweets, and that the former were more diverse in their representation of place. The thesis demonstrates how promotional content on Twitter should be more critically analyzed in concert with expressive and descriptive tweets and geotweets, and that this implies spatial ontologies and data collection methods that consider a place on social media as a discursive construction. This is especially so since Twitter has become increasingly ‘platial’ through internal changes and its entwinement with other social media platforms: changes which require consideration in all Twitter-based spatial and textual analyses. The study provides an updated perspective on Twitter’s use in the spatial humanities, GIScience and geography and contributes to those interested in applying more nuanced cartographies of places

Geo-social media as a proxy for hydrometeorological data for streamflow estimation and to improve flood monitoring

Floods are one of the most devastating types of worldwide disasters in terms of human, economic, and social losses. If authoritative data is scarce, or unavailable for some periods, other sources of information are required to improve streamflow estimation and early flood warnings. Georeferenced social media messages are increasingly being regarded as an alternative source of information for coping with flood risks. However, existing studies have mostly concentrated on the links between geo-social media activity and flooded areas. Thus, there is still a gap in research with regard to the use of social media as a proxy for rainfall-runoff estimations and flood forecasting. To address this, we propose using a transformation function that creates a proxy variable for rainfall by analysing geo-social media messages and rainfall measurements from authoritative sources, which are later incorporated within a hydrological model for streamflow estimation. We found that the combined use of official rainfall values with the social media proxy variable as input for the Probability Distributed Model (PDM), improved streamflow simulations for flood monitoring. The combination of authoritative sources and transformed geo-social media data during flood events achieved a 71% degree of accuracy and a 29% underestimation rate in a comparison made with real streamflow measurements. This is a significant improvement on the respective values of 39% and 58%, achieved when only authoritative data were used for the modelling. This result is clear evidence of the potential use of derived geo-social media data as a proxy for environmental variables for improving flood early-warning systems

High resolution global gridded data for use in population studies

Recent years have seen substantial growth in openly available satellite and other geospatial data layers, which represent a range of metrics relevant to global human population mapping at fine spatial scales. The specifications of such data differ widely and therefore the harmonisation of data layers is a prerequisite to constructing detailed and contemporary spatial datasets which accurately describe population distributions. Such datasets are vital to measure impacts of population growth, monitor change, and plan interventions. To this end the WorldPop Project has produced an open access archive of 3 and 30 arc-second resolution gridded data. Four tiled raster datasets form the basis of the archive: (i) Viewfinder Panoramas topography clipped to Global ADMinistrative area (GADM) coastlines; (ii) a matching ISO 3166 country identification grid; (iii) country area; (iv) and slope layer. Further layers include transport networks, landcover, nightlights, precipitation, travel time to major cities, and waterways. Datasets and production methodology are here described. The archive can be downloaded both from the WorldPop Dataverse Repository and the WorldPop Project websit

A multicriteria optimization framework for the definition of the spatial granularity of urban social media analytics

The spatial analysis of social media data has recently emerged as a significant source of knowledge for urban studies. Most of these analyses are based on an areal unit that is chosen without the support of clear criteria to ensure representativeness with regard to an observed phenomenon. Nonetheless, the results and conclusions that can be drawn from a social media analysis to a great extent depend on the areal unit chosen, since they are faced with the well-known Modifiable Areal Unit Problem. To address this problem, this article adopts a data-driven approach to determine the most suitable areal unit for the analysis of social media data. Our multicriteria optimization framework relies on the Pareto optimality to assess candidate areal units based on a set of user-defined criteria. We examine a case study that is used to investigate rainfall-related tweets and to determine the areal units that optimize spatial autocorrelation patterns through the combined use of indicators of global spatial autocorrelation and the variance of local spatial autocorrelation. The results show that the optimal areal units (30 km2 and 50 km2) provide more consistent spatial patterns than the other areal units and are thus likely to produce more reliable analytical results

Population allocation at the housing unit level: estimates around underground natural gas storage wells in PA, OH, NY, WV, MI, and CA

Background Spatially accurate population data are critical for determining health impacts from many known risk factors. However, the utility of the increasing spatial resolution of disease mapping and environmental exposures is limited by the lack of receptor population data at similar sub-census block spatial scales. Methods Here we apply an innovative method (Population Allocation by Occupied Domicile Estimation – ABODE) to disaggregate U.S. Census populations by allocating an average person per household to geospatially-identified residential housing units (RHU). We considered two possible sources of RHU location data: address point locations and building footprint centroids. We compared the performance of ABODE with the common proportional population allocation (PPA) method for estimating the nighttime residential populations within 200 m radii and setback areas (100 – 300 ft) around active underground natural gas storage (UGS) wells (n = 9834) in six U.S. states. Results Address location data generally outperformed building footprint data in predicting total counts of census residential housing units, with correlations ranging from 0.67 to 0.81 at the census block level. Using residentially-sited addresses only, ABODE estimated upwards of 20,000 physical households with between 48,126 and 53,250 people living within 200 m of active UGS wells – likely encompassing the size of a proposed UGS Wellhead Safety Zone. Across the 9834 active wells assessed, ABODE estimated between 5074 and 10,198 more people living in these areas compare to PPA, and the difference was significant at the individual well level (p = < 0.0001). By either population estimation method, OH exhibits a substantial degree of hyperlocal land use conflict between populations and UGS wells – more so than other states assessed. In some rare cases, population estimates differed by more than 100 people for the small 200 m2 well-areas. ABODE’s explicit accounting of physical households confirmed over 50% of PPA predictions as false positives indicated by non-zero predictions in areas absent physical RHUs. Conclusions Compared to PPA – in allocating identical population data at sub-census block spatial scales –ABODE provides a more precise population at risk (PAR) estimate with higher confidence estimates of populations at greatest risk. 65% of UGS wells occupy residential urban and suburban areas indicating the unique land use conflicts presented by UGS systems that likely continue to experience population encroachment. Overall, ABODE confirms tens of thousands of homes and residents are likely located within the proposed UGS Wellhead Safety Zone – and in some cases within state’s oil and gas well surface setback distances – of active UGS wells

Harnessing social media data to explore urban tourist patterns and the implications for retail location modelling

The tourism landscape in urban destinations has been spatially expanded in recent years due to the increasing prevalence of sharing economy accommodation and other tourism trends. Tourists now mix with locals to form increasingly intricate population geographies within urban neighbourhoods, bringing new demand into areas which are beyond the conventional tourist locations. How these dispersed tourist demands impact local communities has become an emerging issue in both urban and tourism studies. However, progress has been hampered by the lack of fine granular travel data which can be used for understanding urban tourist patterns at the small-area level. Paying special attention to tourist grocery demand in urban destinations, the thesis takes London as the example to present the various sources of LBSN datasets that can be used as valuable supplements to conventional surveys and statistics to produce novel tourist population estimates and new tourist grocery demand layers at the small area level. First, the work examines the potential of Weibo check-in data in London for offering greater insights into the spatial travel patterns of urban tourists from China. Then, AirDNA and Twitter datasets are used in conjunction with tourism surveys and statistics in London to model the small area tourist population maps of different tourist types and generate tourist demand estimates. Finally, Foursquare datasets are utilised to inform tourist grocery travel behaviour and help to calibrate the retail location model. The tourist travel patterns extracted from various LBSN data, at both individual and collective levels, offer tremendous value to assist the construction and calibration of spatial modelling techniques. In this case, the emphasis is on improving retail location spatial Interaction Models (SIMs) within grocery retailing. These models have seen much recent work to add non-residential demand, but demand from urban tourism has yet to be included. The additional tourist demand layer generated in this thesis is incorporated into a new custom-built SIM to assess the impacts of urban tourism on the local grocery sector and support current store operations and trading potential evaluations of future investments