Housing is a key component of urban sustainability. The objective of this study was to assess the significance of key spatial determinants of median home price in towns in Massachusetts that impact sustainable growth. Our analysis investigates the presence or absence of spatial non-stationarity in the relationship between sustainable growth, measured in terms of the relationship between home values and various parameters including the amount of unprotected forest land, residential land, unemployment, education, vehicle ownership, accessibility to commuter rail stations, school district performance, and senior population. We use the standard geographically weighted regression (GWR) and Mixed GWR models to analyze the effects of spatial non-stationarity. Mixed GWR performed better than GWR in terms of Akaike Information Criterion (AIC) values. Our findings highlight the nature and spatial extent of the non-stationary vs. stationary qualities of key environmental and social determinants of median home price. Understanding the key determinants of housing values, such as valuation of green spaces, public school performance metrics, and proximity to public transport, enable towns to use different strategies of sustainable urban planning, while understanding urban housing determinants-such as unemployment and senior population-can help modify urban sustainable housing policies.
With the world population projected to grow significantly over the next few decades, and in the presence of additional stress caused by climate change and urbanization, securing the essential resources of food, energy, and water is one of the most pressing challenges that the world faces today. There is an increasing priority placed by the United Nations (UN) and US federal agencies on efforts to ensure the security of these critical resources, understand their interactions, and address common underlying challenges. At the heart of the technological challenge is
data science applied to environmental data
. The aim of this special publication is the focus on big data science for food, energy, and water systems (FEWSs). We describe a research methodology to frame in the FEWS context, including decision tools to aid policy makers and non-governmental organizations (NGOs) to tackle specific UN Sustainable Development Goals (SDGs). Through this exercise, we aim to improve the “supply chain” of FEWS research, from gathering and analyzing data to decision tools supporting policy makers in addressing FEWS issues in specific contexts. We discuss prior research in each of the segments to highlight shortcomings as well as future research directions.
The United Nations’ Sustainable Development Goal 3 is to ensure health and well-being for all at all ages with a specific target to end malaria by 2030. Aligned with this goal, the primary objective of this study is to determine the effectiveness of utilizing local spatial variations to uncover the statistical relationships between malaria incidence rate and environmental and behavioral factors across the counties of Kenya. Two data sources are used—Kenya Demographic and Health Surveys of 2000, 2005, 2010, and 2015, and the national Malaria Indicator Survey of 2015. The spatial analysis shows clustering of counties with high malaria incidence rate, or hot spots, in the Lake Victoria region and the east coastal area around Mombasa; there are significant clusters of counties with low incidence rate, or cold spot areas in Nairobi. We apply an analysis technique, geographically weighted regression, that helps to better model how environmental and social determinants are related to malaria incidence rate while accounting for the confounding effects of spatial non-stationarity. Some general patterns persist over the four years of observation. We establish that variables including rainfall, proximity to water, vegetation, and population density, show differential impacts on the incidence of malaria in Kenya. The El-Nino–southern oscillation (ENSO) event in 2015 was significant in driving up malaria in the southern region of Lake Victoria compared with prior time-periods. The applied spatial multivariate clustering analysis indicates the significance of social and behavioral survey responses. This study can help build a better spatially explicit predictive model for malaria in Kenya capturing the role and spatial distribution of environmental, social, behavioral, and other characteristics of the households.
Soybean cultivation in China has significantly decreased due to the rising import of genetically modified soybeans from other countries. Understanding soybean's extent and change information is of great value for national agricultural policy implications and global food security. Some previous studies have explored the quantitative relationships between crop area and spectral variables derived from remote sensing data. However, both those linear or non-linear relationships were expressed by global regression models, which ignored the spatial non-stationarity of crop spectral signature and may limit the prediction accuracy. This study presented a geographically weighted regression model (GWR) to estimate fractional soybean at 250 m spatial resolution in Heilongjiang Province, one of the most important food production regions in China, using time-series MODIS data and high-quality calibration information derived from Landsat data. A forward stepwise optimization strategy was embedded with the GWR model to select the optimal subset of independent variables for soybeans. Normalized Difference Vegetation Index (NDVI) of Julian day 233 to 257 when soybeans are filling seed was found to be the most important temporal period for sub-pixel soybean area estimation. Our MODIS-based soybean area compared well with Landsat-based results at pixel-level. Also, there was a good agreement between the MODIS-based result and census data at county level, with the coefficient of determination (R 2) of 0.80 and the root mean square error (RMSE) was 340.21 km 2. Additionally, F-test results showed GWR model had better model goodness-of-fit and higher prediction accuracy than the traditional ordinary least squares (OLS) model. These promising results suggest crop spectral variations both at temporal and spatial scales should be considered when exploring its relationship with pixel-level crop acreage. The optimized GWR model by combining an automated feature selection strategy has great potential for estimating sub-pixel crop area at regional scale based on remote sensing time-series data.
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