This study serves as an exploratory inquiry into the validity of the Compact City paradigm, a legacy of the high-growth era, as a universal standard for non-metropolitan cities confronting the dual pressures of depopulation and climate change. Building upon environmental scenario configurations established in prior research, this study makes an original contribution by introducing a coupled socio-economic simulation using CommunityViz, thereby enabling a quantitative assessment of the trade-off between environmental performance and urban carrying capacity that has not previously been attempted for this study area. Gangneung, a non-metropolitan city in South Korea exhibiting sustained demographic decline, serves as the study site. Three land-use scenarios (Business as Usual, Distributed Urban Development, and Compact City) were simulated and compared using environmental outputs from the InVEST Urban Cooling Model alongside socio-economic metrics generated through CommunityViz. The analysis revealed a potential trade-off between environmental performance and socio-economic vitality. The Compact City scenario excelled in heat mitigation but supported a comparatively lower population and employment capacity. Conversely, the Distributed Urban Development scenario, despite having an identical urbanization footprint, accommodated the highest population and number of commercial jobs through its polycentric spatial structure, suggesting that spatial distribution, rather than density concentration, may better serve urban carrying capacity in a depopulation context. Despite its single-case scope and the absence of statistically definitive evidence, this study provides a conceptual basis for rethinking non-metropolitan urban forms and suggests the possibility that strategic spatial flexibility may offer a more adaptive approach than forced compaction for cities in the post-growth era.
With advances to UAV technology, in particular with regards to digital image sensors, applications of visible and thermal infrared imagery applied to forest ecosystems are increasing. In this case study methods for mapping thermal infrared temperature within hardwood and pine recreational study sites are employed. Absolute forest ecosystem surface temperature values were calculated from UAV obtained digital imagery to compare microclimate habitat within two hiking trails on the Stephen. F. Austin State University Campus. Results show that hardwood study site had a mean temperature that was significantly lower than that of the pine study site, whereas there was no difference between trail area and the entire study site. It indicates that UAV digital thermal infrared data can be used for recreation management purposes. Recreational foresters, with access to thermal infrared UAV data, can make more informed management decisions about the forest resources within their management jurisdiction.
Sincere thanks are extended to the individuals who reviewed manuscripts for International Journal of Geospatial and Environmental Research during the period from 1 January 2021 to 31 December 2022.
Sincere thanks are extended to the individuals who reviewed manuscripts for International Journal of Geospatial and Environmental Research during the period from 1 January 2023 to 31 December 2024.
Band ratios using remote imagery can be useful for monitoring large bodies of water when high quality imagery is available. Sentinel-2 satellite imagery provides frequent, high-resolution coverage of the globe. This study set out to test the usefulness of existing band ratios for estimating chlorophyll a (CHL-a), dissolved organic carbon (DOC), and turbidity with Sentinel-2 imagery. USGS in-situ data was matched to Sentinel-2 imagery of Beaver Lake, Arkansas taken August 2015 to July 2019 and the dark spectrum fitting (DSF) atmospheric correction method in ACOLITE was applied to generate surface reflectance values. CHL-a was estimated using two different methods, the band 5 (B5) peak at 704.1 nm and the ratio of B5 to B4. DOC was estimated using the ratio of B3 to B4. A turbidity estimation equation was created by directly correlating turbidity to B4 reflectance values. The usage of these methods was deemed to be unfit for use under the conditions found at Beaver Lake. Poor correlation was found for CHL-a (R² = 0.0228, R = -0.1510, & R² = 0.0344, R = 0.1855) and for DOC (R² = 0.0548, R = -0.2341). Turbidity was more strongly corelated to the estimate equation (R² = 0.8402, R =0.9166) but considering the poor results for other parameters it is not recommended to apply these methodologies to parameter estimation at Beaver Lake.
COVID-19 has affected all aspects of global activity and has since reshaped and restructured society itself. In particular, real estate has experienced numerous changes in composition since March of 2020. This study examined the early effects of COVID-19 on New York City’s real estate market through a social equity lens. Real estate dynamics and socioeconomic characteristics in New York City’s metropolitan statistical area (MSA) were analyzed through geographically weighted regressions (GWRs) on the period following the outbreak in the United States. The results suggest that there was a preference towards lower-density neighborhoods in the face of the contact-dependent COVID-19 pandemic. It is also evident that the more socially vulnerable areas were negatively impacted the most, with education levels and public transportation dependence being key factors in driving vulnerability. This study’s contributions lie in the use of open-source real estate data as an effective metric for investigating social disparities in the face of an external shock. The work also supports the notion of social vulnerability being a driver of the negative and lasting effects of a disaster. Policymakers and urban planners ought to take socially vulnerable populations into consideration when addressing future disaster response and preparedness.
Spatial autocorrelation in model residuals can have a significant impact on the results of spatial or space-time models. This can result in misleading estimates of the influence of different factors, potentially exaggerating or even reversing the perceived effects of these factors. This study also considers the potential implications of the Modifiable Areal Unit Problem (MAUP) in the context of spatial-temporal models. In this case study for southeastern Ghana, we examined whether and how spatial autocorrelation in model residuals might generate bias in regression coefficients when explaining women’s body mass index (BMI) across urban and rural areas. Eigenvector spatial filtering, with various settings of influential zones, was systematically tested in a latent trajectory model to detect the impacts of spatial autocorrelation.
COVID-19, originally reported in China, has brought an increase in anti-Asian and Asian American hate incidents and crimes in the United States. However, research on hate incidents and crimes are relatively new in the field of geography. To provide better ways to investigate hate crime incidents against Asians and Asian Americans during COVID-19, this article draws on various research methods from existing studies on hate crimes. Geographers have focused attention on minority groups linked to different geographic scales, and non-geographic studies have focused mainly on psychological symptoms and impacts on health. Even though existing studies have helped broaden the knowledge of the subject, the geographic aspects of the issue require further examination. This article suggests that geographers should pay more attention to four aspects of research in hate crimes and incidents for future research: avoiding oversimplified concepts, reconsidering relational aspects within the local community, identifying intersectionality and everydayness of people, and engaging more with the practice of the law enforcement and the local communities.
As part of natural resource education in the Arthur Temple College of Forestry and Agriculture at Stephen F. Austin State University (SFASU), students were instructed to take areal and linear measurements of grounds remotely using available platforms including aerial orthomosaic derived from UAS (unmanned aerial system) acquired imagery, Google Earth Pro, and Pictometry. The onscreen measurement was conducted at five different map scales, 1/1000, 1/2000, 1/3000, 1/4000, and 1/5000. Accuracy of the measurements was assessed by comparing the onscreen measurements to ground truth data verified with a measuring tape. Results show that measurements based on the UAS were more accurate than other platforms at all scales, resulting in lower RMSE (root mean square error). However, this advantage diminished when the scale approached 1/5000 where features were too small to identify onscreen. This scale related accuracy is more profound with Google Earth Pro. Overall, all three platforms performed its best at the 1/1000 scale, while accuracy decreased when an image was zoomed out to a smaller scale. All three platforms can be used with confidence at the 1/3000 scale or larger such as 1/1000 or 1/2000. For linear measurements, UAS was significantly more accurate than others. For areal measurements, Pictometry was significantly less accurate than others.
This study evaluated two popular software packages currently used within the natural resources profession to create orthophoto mosaics: Drone2Map and Pix4Dmapper. Of particular concern was how effective these two software packages would perform in creating orthophoto mosaics over a city park in East Texas consisting of forest, open grass, and urban concrete surrounding a lake. Two drone flights over the city park were conducted. One flight was at 76 meters (250 feet) above ground with a single pass configuration. The other flight was at 122 meters (400 feet) above ground with a double pass configuration. Upon the completion of each drone flight, two orthophoto mosaics were created for each flight using all images acquired per flight with Drone2Map and Pix4Dmapper software. For the single pass configuration Drone2Map failed to complete a basic orthophoto mosaic.
