Satellite and Ground Estimates of Surface and Canopy-Layer Urban Heat Island

The urban heat island (UHI) effect is one of the prominent impacts of urbanization that affects human health and energy consumption. As the data is limited and inconsistent, UHI comparative studies between UHIUCL and UHISurf on the seasonal scale are limited. The use of only daytime summer imagery reporting “Inverted UHI” undermines the holistic view of the phenomenon. Therefore, this study analyses the seasonal patterns for UHISurf and UHIUCL in three climate zones (Delhi, Pune, and Montreal). The three cities experience a high traditional night-time UHIUCL (Delhi 7°C, Pune 6°C, Montreal 1.89°C). Landsat captures a prominent daytime UHISurf (15°C) in Montreal with temperate climate and daytime inverted UHISurf (-4°C) for Delhi in summer. Seasonally, the night-time UHI is prominent in summer and monsoon for Delhi, summer and spring for Pune, and summer for Montreal. Due to UHI effect, the heatwaves can be more intense in semi-arid and tropical cities than temperate cities.

Using Combination Technique for Land Cover Classification of Optical Multispectral Images

The need for efficient planning of the land is exponentially increasing because of the unplanned human activities, especially in the urban areas. A land cover map gives a detailed report on temporal dynamics of a given geographical area. The land cover map can be obtained by using machine learning classifiers on the raw satellite images. In this work, the authors propose a combination method for the land cover classification. This method combines the outputs of two classifiers, namely, random forests (RF) and support vector machines (SVM), using Dempster-Shafer combination theory (DSCT), also called the theory of evidence. This combination is possible because of the inherent uncertainties associated with the output of each classifier. The experimental results indicate an improved accuracy (89.6%, kappa = 0.86 as versus accuracy of RF [87.31%, kappa = 0.83] and SVM [82.144%, kappa = 0.76]). The results are validated using the normalized difference vegetation index (NDVI), and the overall accuracy (OA) has been used as a comparison basis.

The Effect of Socioeconomic and Environmental Factors on Obesity

This paper examined the effect of socio-economic and environmental factors on obesity in Cleveland (Ohio) using an OLS model and three spatial regression models: spatial error model, spatial lag model, and a spatial error model with a spatially lagged response (SEMSLR). Comparative assessment of the models showed that the SEMSLR and the spatial error models were the best models. The spatial effect from the various spatial regression models was statistically significant, indicating an essential spatial interaction among neighboring geographic units and the need to account for spatial dependency in obesity research. The authors also found a statistically significant positive association between the percentage of families below poverty, Black population, and SNAP recipient with obesity rate. The percentage of college-educated had a statistically significant negative association with the obesity rate. The study shows that health outcomes such as obesity are not randomly distributed but are more clustered in deprived and marginalized neighborhoods.

Geospatial Analysis of Agriculture Land Loss in Qena City, Upper Egypt

Although urbanization presents opportunities for new urban developments, it may have serious problems on environment and land use/cover patterns. The present study aims to evaluate the performance of built‑up delineation index set (BDIS) for mapping agricultural land loss in Upper Egypt. Three Landsat images were obtained for the years 1986, 2000, and 2016 and utilized as inputs to calculate the BDIS variables. Then a supervised classification technique (i.e., support vector machine) was used to classify the images. The findings showed that urban areas have witnessed a dramatic expansion at a growing rate of 44.1% during the 30 years. As a result, the loss of the agricultural land was found to be approximately 64.83 ha, which represents -4%, during the same period because of the urban expansion and the illegal construction of settlements. These findings would support the local decision makers in urban and agriculture land management authorities to develop sustainable development plans that control the spatiotemporal urban expansion and agricultural land loss.

A Visual Insight Into Tweeting Activity Before and During Natural Disasters

Big data provided by social media has been increasingly used in various fields of research including disaster studies and emergency management. Effective data visualization plays a central role in generating meaningful insight from big data. However, big data visualization has been a challenge due to the high complexity and high dimensionality of it. The purpose of this study is to examine how the number and spatial distribution of tweets changed on the day Hurricane Harvey made landfall near Houston, Texas. For this purpose, this study analyzed the change in tweeting activity between the Friday of Hurricane Harvey and a typical Friday before the event.

Assessment of Fishers' Knowledge and Willingness to Adopt Seaweed Cultivation in Ghana in Relation to Gender, Age, Education, and Distance

The aim of this study was to assess the knowledge and willingness of fishers in four selected fishing communities (Ayitepa/Kponor, Ngyiresia, Adjoa, and Miemia) in Ghana for seaweed cultivation in terms of gender, age, education, and distance. The study used both qualitative and quantitative approaches to collect data and household spatial position recorded by the global positioning system (GPS). The findings show that an average of 95.8% of fishers knew seaweed and 86.5% were willing cultivate it. A chi-square test shows no significant association between gender, age, education, distance, and the willingness of fishers to participate in the seaweed cultivation. In a regression model, only age group between the ages of 18 and 64 have a strong effect on the willingness of fishers to grow seaweed (P<0.05). However, focus group interviews with fishers indicate that women are not allowed to engage in any offshore activities at Ayitepa/Kponor, Ngyiresia, and Adjoa. This study shows that gender and age are main factors in deciding human capital for the cultivation of seaweed in Ghana.

Threats of Changes in Land-Use and Drivers on Owabi and Barekese Watershed Forests in Ghana

This study analyzes the patterns of land-use and land-cover changes for the last three decades (1986–2017) and its drivers for Owabi and Barekese watersheds in the moist semi-deciduous forest of Ghana. The study used Landsat satellite imageries of 1986, 1998, 2007, and 2017 and population data to analyze land cover and use changes of the two watersheds. A decline in natural vegetation cover by 57% and 71.3% has occurred for Owabi and Barekese watersheds respectively. Cropland increased by 77.1% and 105.2% while settlement has increased by 1,018% and 4%, respectively, for Owabi and Barekese watersheds. Cropland is the main form of land-use change for Barekese watershed while settlement is the main land-use change in the Owabi watershed. Annual expansion of settlement within the Owabi site was 38.1%, and cropland was 5.2% for the Barekese site. Population trends had a significant negative relationship with forest cover and a positive relationship with settlement and cropland. Catchment degradation was also influenced by the management model used.

Relationships and Spatial Structure of Soil Physical Properties in a Ten-Year Corn-Cotton Rotation Field

This study quantified the relationships between soil, textural, and hydraulic properties at the field-scale for a conventional tilled Memphis silt loam that had undergone a 10-year corn and cotton rotation and described their spatial variability. Composite soil samples collected from the plow layer at 272 nodes on 15 x 15 m grids were analyzed for texture and bulk density. These values were used as pedotransfer functions to predict unsaturated (Ko) and saturated hydraulic (Ks) conductivities as well as the van Genuchten curve shape parameters α and n. Regression analyses quantified relationships between the measured and model predicted soil properties. While correlations between textural and model predicted soil properties including bulk density were significant (p<0.05), those between sand and clay, clay and n, clay and α were not. Sand and silt appeared to be better predictors of soil hydraulic conductivity and the van Genuchten curve shape parameters for the soil investigated. Spatial dependence was strong for sand, silt, bulk density, Ko, α and n, and moderate for clay and Ks.

A Community Area Perspective of Real Estate and Crime in Chicago

This paper examines the relationships between crime-types and property values in the community areas of Chicago. Using a variety of unconventional web-based data sources, the authors use correlations, mapping, and regression analyses to find that while crime generally associates negatively with property values, not all crime-types have similar effects. Lower incidence of violent crimes and sex offenders in neighborhoods can have pronounced positive impacts on property values whereas certain types of property crimes gravitate toward neighborhoods with expensive homes. Further, crime rates may be similar or even higher than those in cheaper/disadvantaged areas. These types of offenses do not necessarily follow the price-dropping effect like other crime-types do on housing values. However, property crimes such as thefts do not follow this trend. They, thus, recommend that property crime alone should not be a factor when making decisions concerning home buying and/or where to live.

Estimation of Soil Erosion in Three Northern Regions of Ghana Using RUSLE in GIS Environment

Soil erosion is a global problem with severe consequences, which has become a widespread environmental challenge in the northern parts of Ghana in recent times. This research integrated RUSLE into GIS to estimate the annual soil erosion rates for the Northern, North-East, and Savannah Regions of Ghana. A soil erosion map was generated with an annual soil erosion rate of 4.0 t ha−1y−1 for the Northern Region, 5.0 t ha−1y−1 for the North-East Region, and 7.0 t ha−1y−1 for the Savannah Region. Relatively higher erosion rates were observed in the Tatale Sangule and Kpandai districts of the Northern Region, with rainfall erosivity being the main driving factor. There was a landuse/cover erosion reduction effect of 66% in the Northern Region, 70% in the Northeast Region, and 58% in the Savannah Region. The cover management (C) factor was overwhelmingly the main erosion-reducing factor in erosion control as opposed to land conservation (P) factor.