In this commentary, we describe the current state of the art of points of interest (POIs) as digital, spatial datasets, both in terms of their quality and affordings, and how they are used across research domains. We argue that good spatial coverage and high-quality POI features — especially POI category and temporality information — are key for creating reliable data. We list challenges in POI geolocation and spatial representation, data fidelity, and POI attributes, and address how these challenges may affect the results of geospatial analyses of the built environment for applications in public health, urban planning, sustainable development, mobility, community studies, and sociology. This commentary is intended to shed more light on the importance of POIs both as standalone spatial datasets and as input to geospatial analyses.

Remote sensing techniques have been shown effective for large-scale damagesurveys after a hazardous event in both near real-time or post-event analyses. The paperaims to compare accuracy of common imaging processing techniques to detect tornadodamage tracks from Landsat TM data. We employed the direct change detection approachusing two sets of images acquired before and after the tornado event to produce a principalcomponent composite images and a set of image difference bands. Techniques in thecomparison include supervised classification, unsupervised classification, and object-oriented classification approach with a nearest neighbor classifier. Accuracy assessment isbased on Kappa coefficient calculated from error matrices which cross tabulate correctlyidentified cells on the TM image and commission and omission errors in the result. Overall,the Object-oriented Approach exhibits the highest degree of accuracy in tornado damagedetection. PCA and Image Differencing methods show comparable outcomes. Whileselected PCs can improve detection accuracy 5 to 10%, the Object-oriented Approachperforms significantly better with 15-20% higher accuracy than the other two techniques.

Remote sensing (RS) and geographic information systems (GIS) techniques are applied to high-resolution satellite imagery to determine characteristics of tornado damage from the 3 May 1999 tornado outbreak. Three remote sensing methods, including principal components analysis, normalized difference vegetation index (NDVI) analysis, and NDVI change analysis, elicit tornado damage paths at different levels of detail on the 23.5-m-resolution images captured by the Linear Imaging Self-Scanning III (LISS-3) sensor on the Indian Remote Sensing (IRS) satellite before and after the outbreak. Remote sensing results were spatially overlaid on F-scale contours compiled by the members of Oklahoma Weather Center. Spatial overlays reveal that results from the principal components analysis correlate well with F3 or greater damage. NDVI analysis shows signatures expanding to F2 damage, and NDVI change analysis is capable of detecting F1 damage in some instances. In general, results of these analyses correspond to... Abstract Remote sensing (RS) and geographic information systems (GIS) techniques are applied to high-resolution satellite imagery to determine characteristics of tornado damage from the 3 May 1999 tornado outbreak. Three remote sensing methods, including principal components analysis, normalized difference vegetation index (NDVI) analysis, and NDVI change analysis, elicit tornado damage paths at different levels of detail on the 23.5-m-resolution images captured by the Linear Imaging Self-Scanning III (LISS-3) sensor on the Indian Remote Sensing (IRS) satellite before and after the outbreak. Remote sensing results were spatially overlaid on F-scale contours compiled by the members of Oklahoma Weather Center. Spatial overlays reveal that results from the principal components analysis correlate well with F3 or greater damage. NDVI analysis shows signatures expanding to F2 damage, and NDVI change analysis is capable of detecting F1 damage in some instances. In general, results of these analyses correspond to...

From a public health perspective, a healthier community environment correlates with fewer occurrences of chronic or infectious diseases. Our premise is that community health is a non-linear function of environmental and socioeconomic effects that are not normally distributed among communities. The objective was to integrate multivariate data sets representing social, economic, and physical environmental factors to evaluate the hypothesis that communities with similar environmental characteristics exhibit similar distributions of disease.

The United Nations General Assembly adopted the 2030 Agenda for sustainable development goal in 2015 for a prosperous and inclusive future for all. Reflecting on the contributions of IJGIS publications to sustainability, this editorial draws the intellectual relevance of GIScience to the UN sustainable development goals (SDGs). GIScience, with its body of GIScience knowledge and knowledge production, has much to contribute to addressing the shortfalls recognized by the 2019 UN High-Level Political Forum on Sustainable Development. The COVID-19 pandemic threatens sustainability in many ways, but it also signifies the decreases of environmental pollutants by reducing human activities and elicits the latent social and health inequalities. This editorial highlights opportunities for GIScience contributions to the UN SDGs, outlines research challenges of the UN SDGs to GIScience, and welcomes submissions of GIScience research on UN SDGs to IJGIS.