Predictive mapping of soil total nitrogen at a regional scale: A comparison between geographically weighted regression and cokriging

Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R² for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected.     

Variation in low food access areas due to data source inaccuracies

Several spatial measures of community food access identifying so called “food deserts” have been developed based on geospatial information and commercially-available, secondary data listings of food retail outlets. It is not known how data inaccuracies influence the designation of Census tracts as areas of low access. This study replicated the U.S. Department of Agriculture Economic Research Service (USDA ERS) food desert measure and the Centers for Disease Control and Prevention (CDC) non-healthier food retail tract measure in two secondary data sources (InfoUSA and Dun & Bradstreet) and reference data from an eight-county field census covering 169 Census tracts in South Carolina. For the USDA ERS food deserts measure accuracy statistics for secondary data sources were 94% concordance, 50–65% sensitivity, and 60–64% positive predictive value (PPV). Based on the CDC non-healthier food retail tracts both secondary data demonstrated 88–91% concordance, 80–86% sensitivity and 78–82% PPV. While inaccuracies in secondary data sources used to identify low food access areas may be acceptable for large-scale surveillance, verification with field work is advisable for local community efforts aimed at identifying and improving food access.     

A comparison of techniques for generating forest ownership spatial products

To fully understand forest resources, it is imperative to understand the social context in which the forests exist. A pivotal part of that context is the forest ownership. It is the owners, operating within biophysical and social constraints, who ultimately decide if the land will remain forested, how the resources will be used, and by whom. Forest ownership patterns vary substantially across the United States. These distributions are traditionally represented with tabular statistics that fail to capture the spatial patterns of ownership. Existing spatial products are not sufficient for many strategic-level planning needs because they are not electronically available for large areas (e.g., parcels maps) or do not provide detailed ownership categories (e.g., only depict private versus public ownership). Thiessen polygon, multinomial logit, and classification tree methods were tested for producing a forest ownership spatial dataset across four states with divergent ownership patterns: Alabama, Arizona, Michigan, and Oregon. Over 17,000 sample points with classified forest ownership, collected as part of the USDA Forest Service, Forest Inventory and Analysis (FIA) program, were divided into two datasets, one used as the dependent variable across all of the models and 10 percent of the points were retained for validation across the models. Additional model inputs included a polygon coverage of public lands from the Conservation Biology Institute’s Protected Areas Database (PAD) and data representing human population pressures, road densities, forest characteristics, land cover, and other attributes. The Thiessen polygon approach predicted ownership patterns based on proximity to the sample points in the model dataset and subsequent combining with the PAD ownership data layer. The multinomial logit and classification tree approaches predicted the ownership at the validation points based on the PAD ownership information and data representing human population, road, forest, land cover, and other attributes. The percentage of validation points across the four states correctly predicted ranged from 76.3 to 78.9 among the methods with corresponding weighted kappa values ranging from 0.73 to 0.76. Different methods performed slightly, but statistically significantly, better in different states Overall, the Thiessen polygon method was deemed preferable because: it has a lower bias towards dominant ownership categories; requires fewer inputs; and is simpler to implement.     

A spatio-temporal analysis of academic performance at the Basic Education Certificate Examination in Ghana

Over the last decade, Ghana has more than tripled investment in its basic education system. Consequently, the country has made huge educational gains, primarily in providing universal access to basic education. However, many stakeholders are worried that academic performance is lagging because of disproportional attention to accessing basic education. Discussion of these concerns is hampered by ongoing disagreement about the true trajectory of academic performance at the basic education level and the widespread nature of students' lagging academic performance. In part, this disagreement stems from the failure of empirical studies to comprehensively examine trends in academic performance standards at the basic education level by concurrently considering a geographical and longitudinal perspective. Thus, this study examines the spatio-temporal trends of academic performance at the junior high school level since 2009 by using multilevel growth curve modeling, spatial statistics, and district-level longitudinal data. Results reveal 3 statistically distinct trajectories of academic performance: erratic, accelerating, and decelerating changes. Results also show that rural–urban gaps explain 31% of the performance trajectories, a trend which is expected to persist in the long term. In addition, we find extreme variations in academic performance within rural areas. Given the varying trajectories and geographical variability in academic performance, we suggest a localized approach to addressing challenges of low academic achievement at the basic education level in Ghana.     

Empirical models of rain-spells characteristics – A case study of a Mediterranean-arid climatic transect

Environmental (geomorphological, hydrological and ecological) processes are controlled by rainfall, particularly in the Mediterranean, semi-arid and arid regions. Rainfall was analyzed using the concept of rain-spells, i.e., a period of successive rain days preceded and followed by at least one day without rainfall. Daily data from 13 stations along a climatic transect extending from the Judean Mountains with a Mediterranean climate to the Dead Sea arid region in Israel were studied. Rain-spell characteristics (number, yield and duration), based on these data, are presented for different rainfall thresholds, which might be used for different environmental processes such as rock weathering, soil organic matter dynamics, landslides, overland flow and floods and soil erosion. Three estimation models have been developed in order to predict the mean annual Number of Rain-Spells (NRS), mean Rain-Spell Yield (RSY), and mean Rain-Spell Duration (RSD) for the mean annual rainfall and for any given rainfall threshold. These models can be used for current climatic conditions and for scenarios in which the rainfall total changes.     

Linking pesticides and human health: A geographic information system (GIS) and Landsat remote sensing method to estimate agricultural pesticide exposure

Accurate pesticide exposure estimation is integral to epidemiologic studies elucidating the role of pesticides in human health. Humans can be exposed to pesticides via residential proximity to agricultural pesticide applications (drift). We present an improved geographic information system (GIS) and remote sensing method, the Landsat method, to estimate agricultural pesticide exposure through matching pesticide applications to crops classified from temporally concurrent Landsat satellite remote sensing images in California. The image classification method utilizes Normalized Difference Vegetation Index (NDVI) values in a combined maximum likelihood classification and per-field (using segments) approach. Pesticide exposure is estimated according to pesticide-treated crop fields intersecting 500 m buffers around geocoded locations (e.g., residences) in a GIS. Study results demonstrate that the Landsat method can improve GIS-based pesticide exposure estimation by matching more pesticide applications to crops (especially temporary crops) classified using temporally concurrent Landsat images compared to the standard method that relies on infrequently updated land use survey (LUS) crop data. The Landsat method can be used in epidemiologic studies to reconstruct past individual-level exposure to specific pesticides according to where individuals are located.     

Climate-induced change of environmentally defined floristic domains: A conservation based vulnerability framework

Global climate change is having marked influences on species distributions, phenology and ecosystem composition and raises questions as to the effectiveness of current conservation strategies. Conservation planning has only recently begun to adequately account for dynamic threats such as climate change. We propose a method to incorporate climate-dynamic environmental domains, identified using specific environmental correlates of floristic composition, into conservation strategies, using the province of KwaZulu-Natal, South Africa as a case study. The environmental domains offer an approach to conservation that conserves diversity under current and future climates, recognising that the species constituting diversity may change through time. We mapped current locations of domains by identifying their positions in a multi-dimensional environmental space using a non-hierarchical iterative k-means clustering algorithm. Their future locations were explored using an ensemble of future climate scenarios. The HadCM2 and GFDL2.1 models represented the extreme ranges of the models. The magnitude of change in each environmental domain was calculated using Euclidean distances to determine areas of greatest and least stability for each future climate projection. Domains occurring in the savanna biome increase at the expense of domains occurring in the grassland biome, which has significant negative consequences for the species rich grasslands. The magnitude of change maps represents areas of changed climatic conditions or edaphic disjunctions. The HadCM2 model predicted the greatest overall magnitude of change across the province. Species with specific soil requirements may not be able to track changing climatic conditions. A vulnerability framework was developed that incorporated climatic stability and habitat intactness indices. The mean magnitude of change informed the potential speed of transition of domains between the vulnerability quadrants. The framework informs appropriate conservation actions to mitigate climate change impacts on biodiversity. The study explicitly links floristic pattern and climate variability and provides useful insights to facilitate conservation planning for climate change.     

Lessons learned from the 2013 Calgary flood: Assessing risk of drinking water well contamination

A devastating flood occurred in southern Alberta on June 19, 2013, from greater than normal snowfalls in the Rocky Mountains and excess precipitation during the early spring that left soils saturated and unable to absorb any additional precipitation. This flood was Canada's most costly natural disaster, with five to six billion Canadian dollars in damages. The first objective of this study was to determine if the flood caused an increase in private drinking water well contamination in the Calgary Health Zone by comparing contamination rates to previous years. The second objective was to determine which environmental factors were associated with contamination during this flood event. Test results of total coliforms (TC) and Escherichia coli (EC) of private water wells were used to determine contamination. A geographically weighted Poisson regression analysis suggested that TC contamination was not associated with this flood. The EC contamination is positively associated with floodways, flood fringe, farms, and negatively associated with intermittent water (sloughs). These results suggest that for the 2013 flood, individual well characteristics are more important than surrounding geographic features. Thus, it is recommended that homeowners who live in a high-risk area ensure their wells are properly maintained to reduce risk of water well contamination.     

Modelling collective Yawuru values along the foreshore of Roebuck Bay,Western Australia using fuzzy logic

The cadastral model has played a key role in Indigenous dispossession in settler states. Yet, the recognition of Indigenous land rights, which has increased globally since the 1960s, frequently requires Indigenous communities to directly engage with this spatial model. In Australia, native title claimants must use entity-based models of space to delineate their traditional territories during the claim process. They must also engage with planning and development documents which use the cadastral model of space to assert and defend their rights following native title recognition. This is often problematic as Indigenous spatial ontologies emphasise complexity and continuity which is inimical to the ‘crisp’ representations of cadastral space.This study explores the potential of a fuzzy index modelling approach to represent cultural values using a case study from Broome, Western Australia. Sketch mapping, fuzzy index modelling and combinatory techniques were used to produce a model of several cultural values held by the Yawuru community for the in-town foreshore of Roebuck Bay. This model was overlaid on local planning documents to provide the Yawuru community with strategic intelligence for post native title governance. The experience of co-producing this model suggests that such techniques may assist Indigenous communities to engage with settler structures. This implies that policies which fail to extend analytical capacity to interested native title groups as part of programmes of spatial enablement continue to perpetuate historical processes of colonial domination.     

Boundary-based analysis for the assessment of coastal dune landscape integrity over time

Coastal dunes are transitional systems which offer manifold ecosystem services, including material and intangible benefits, whose relevance for human society has been little investigated to date. In order to plan a sustainable use of these threatened ecosystems, proper indicators to quantify ecological services and functions over time should be provided. Since the functionality of coastal dunes is affected by the specific contiguity among different dune habitat patches, in this paper we propose the use of landscape integrity, expressed by the number and length of boundary types among the aforementioned habitats, as an ecosystem functionality indicator. In particular, we aim at properly expressing the trend of coastal dune mosaic integrity in two Italian LTER sites over the last sixty years by applying a generalized diversity function on the number and length of boundary types. Such functions, unlike a traditional diversity index, allow a complete summarization of landscape diversity and structure. The proposed method has proved to be effective in the considered context, because it has adequately underlined the different landscape integrity trend recorded in the two sites. In particular, generalized diversity functions showed to efficiently express both rough and moderate anthropogenic transformations, which affected spatial heterogeneity and functionality of the considered coastal dune mosaic. Further applications at different scales and across different ecosystems are encouraged.     

Benefits of mycorrhizal inoculation in reintroduction of endangered plant species under drought conditions

Numerous human activities constitute threats to biodiversity. The effects of climate change, including increasing drought in already arid lands, pose an additional layer of uncertainty in the fate of rare species. In the case of plants, reintroduction is becoming an important active management practice in species conservation. We hypothesized that even under extreme drought inoculation with mycorrhizal fungi would increase growth rates of an endangered plant in experimental reintroduction. We selected a plant species, Abronia macrocarpa, and conducted the experiment in Texas while the area was experiencing mild and extreme drought intensities. Treatment plots were planted with seed inoculated with arbuscular mycorrhizal fungi and control plots were planted with seed coated with autoclaved inoculant. We analyzed measurements of growth and development of germinated plants. Mean number of leaves was greater in treatment plants (P = 0.005) and mean aerial diameter was larger in treatment plants (P = 0.02) than in control plants. Significantly improved growth suggests that inoculation is a viable technique to increase reintroduction success in plant species especially during periods of drought.     

Soil seed bank longevity of the exotic annual grass Bromus rubens in the Mojave Desert,USA

Although arid environments are often considered among the least invaded terrestrial biomes, the impacts of exotic plant species can be severe and long lasting. Bromus rubens (red brome) is an exotic annual grass species in the Mojave Desert known to outcompete native plant species, alter habitat, and promote accumulation of fuel that contributes to increasing fire frequency and severity. We assessed longevity of the exotic B. rubens seeds in the soil by burying seeds at four depths (0, 2, 5, and 10 cm) and recovering seeds 6, 12, 18, and 24 months after burial. Seed viability was reduced with greater burial depth and greater time since burial. A relatively small proportion of seeds retained viability for two years, suggesting that while the B. rubens seed bank can be large, it is relatively short-lived. Although B. rubens apparently relies more on the annual production, dispersal, and germination of seeds than on a long-lived seed bank for its annual recruitment, the numerous seeds produced by individual plants indicate that even a small proportion of seeds remaining viable for more than a year can aid recruitment from the seed bank and is an important factor in understanding population dynamics.     

都市新区住宅地价空间异质性驱动因素研究 ——基于空间扩展模型和GWR模型的对比

以南京市江宁区为例,基于2004~2011年住宅用地出让数据,利用空间扩展模型和GWR模型对都市新区住宅地价空间异质性及其驱动因素进行研究。结果表明：① 空间扩展模型与GWR模型分别可解释采样区63%、61%的住宅地价变化,较全局回归模型(47%)有显著提升,更有利于研究土地市场的空间异质性。② 空间扩展模型可有效表征各解释变量及其交互项对住宅地价作用的空间结构总体趋势,其拟合效果相对较优。GWR模型则在局部参数估计方面存在优势,借助GIS可将各变量的地价作用模式可视化,从而比空间扩展模型更能有效刻画住宅地价影响因素的空间非平稳性特征,各因素对地价的平均边际贡献排序为水域> 地铁> 大学园区> CBD> 商业网点> 医院,且商业网点、 医院系数值具有方向差异性。③ 距地铁站点、水域、大学园区以及CBD的距离是研究区住宅地价的关键驱动因素,各自存在特有的地价空间作用模式,可为研究区住宅土地市场细分提供科学依据。     

合肥市商业地价驱动因素的空间非平稳性分析

基于微观因素的视角,对合肥市商业地价的空间格局、空间异质性进行空间插值和回归分析,研究发现： ① 商业地价空间格局呈现多中心的结构,峰值区集中分布在城市中心,低值区分布在二环线以外的地区;② GWR模型能清晰地反映出各因素对地价影响力的空间差异,容积率、交通站点、CBD和公园是研究区商业地价的关键驱动因素;③ 较之外在因素,内在因素对商业地价的边际作用效率较大,其中尤以容积率最为突出;④ 相较于欠发达地区,容积率在高度发达的区域对地价的正向效应更为显著,而交通站点和CBD对地价的作用正好相反。商业开发更愿意为小型公园支付更高的价格。     

Multi-scale analysis of spatially varying relationships between agricultural landscape patterns and urbanization using geographically weighted regression

Scientific interpretation of the relationships between agricultural landscape patterns and urbanization is important for ecological planning and management. Ordinary least squares (OLS) regression is the primary statistical method in previous studies. However, this global regression lacks the ability to uncover some local-specific relationships and spatial autocorrelation in model residuals. This study employed geographically weighted regression (GWR) to examine the spatially varying relationships between several urbanization indicators (urbanization intensity index, distance to urban centers and distance to road) and changes in metrics describing agricultural landscape patterns (total area, patch density, perimeter area ratio distribution and aggregation index) at two block scales (5 km and 10 km). Results denoted that GWR was more powerful than OLS in interpreting relationships between agricultural landscape patterns and urbanization, since GWR was characterized by higher adjust R², lower Akaike Information Criterion values and reduced spatial autocorrelations in model residuals. Character and strength of the relationships identified by GWR varied spatially. In addition, GWR results were scale-dependent and scale effects were particularly significant in three aspects: kernel bandwidth of weight determination, block scale of pattern analysis, and window size of local variance analysis. Homogeneity and heterogeneity in the relationships between agricultural landscape patterns and urbanization were subject to the coupled influences of the three scale effects. We argue that the spatially varying relationships between agricultural landscape patterns and urbanization are not accidental but nearly universal. This study demonstrated that GWR has the potential to provide references for ecological planners and managers to address agricultural landscapes issues at all scales.     

Photosynthetic characteristics of sinking microalgae under the sea ice

The photosynthetic characteristics of sinking a microalgal community were studied to compare with the ice algal community in the sea ice and the phytoplankton community in the water column under the sea ice at the beginning of the light season in the first-year sea ice ecosystem on the Mackenzie Shelf, in the western Canadian Arctic. The phytoplankton community was collected using a water bottle, whereas the sinking algal community was collected using particle collectors, and the ice algal community was obtained by using an ice-core sampler from the bottom portion of ice core. Photosynthesis versus irradiance (P-E) incubation experiments were conducted on deck to obtain the initial slope (α^B) and the maximum photosynthetic rate (P_m^B) of the three algal communities. The α^B and the P_m^B of the light saturation curve, and chlorophyll a (Chl a) specific absorption coefficient (ā_ph*) between the sinking microalgal community and the ice algal community were similar and were distinctly different from the phytoplankton community. The significant linear relationship between α^B and P_m^B, which was obtained among the three groups, may suggest that a photo-acclimation strategy is common for all algal communities under the low light regime of the early season. Although the sinking algal community could be held for the entire duration of deployment at maximum, this community remained photosynthetically active once exposed to light. This response suggests that sinking algal communities can be the seed population, which results in a subsequent phytoplankton bloom under the sea ice or in a surface layer, as well as representing food for the higher trophic level consumers in the Arctic Ocean even before the receding of the sea ice.     

基于空间扩展模型和地理加权回归模型的城市住房价格空间分异比较

鉴于已有研究主要集中探讨住房价格的空间依赖性,较少涉及空间异质性对住房特征价格的影响,也很少尝试构建不同计量模型来比较模型间刻画住房价格影响因素空间分异的准确性,以长沙市中心城区为研究区,采用空间扩展模型和地理加权回归模型比较分析城市住房价格影响因素的空间分异,结果表明：① 空间扩展模型和地理加权回归模型都表明,长沙市中心城区的住房属性边际价格随着区位的变化而变化,揭示住房价格影响因素具有显著的空间异质性;小区环境、交通条件、教育配套、生活设施等因素对住房价格的影响强度存在明显的空间分异。② 地理加权回归模型和空间扩展模型都能对传统特征价格模型进行改进,但地理加权回归模型在解释能力和精度方面都超过空间扩展模型;对属性系数估计空间模式的分析,地理加权回归模型形成的结果比采用坐标多义扩展的空间扩展模型更为复杂和直观。     

兰州市主城区房价分异及驱动因素研究

以2021年兰州市主城区678个居住小区房价数据为基础,引入地理场模型量化影响房价的外部因素,通过空间自相关分析、多尺度地理加权回归等模型对房价分异的空间格局及驱动因素的作用机理、带宽差异展开研究,以期为推动河谷型城市房产市场的公平发展提供参考。结果表明：（1） 兰州市主城区平均房价为13739元·m^-2,空间上呈现“一主三副”的带状多中心组团式分布格局,房价由多中心向四周递减,价格相似的小区在地理空间上邻近分布,具有“小集中、大分散”的局部空间特征。（2） 房价分异是多种驱动因素共同作用的结果,区位特征中的主商圈对房价的影响居于首位,建筑特征中的房龄、容积率和邻里特征中的中学数量、绿化率等对房价的影响较大,城市地理特征对房价具有显著影响,愈靠近黄河的小区、房价越高。（3） 各驱动因素的带宽差异明显,主商圈、医院等小尺度变量存在高度空间异质性,而容积率、黄河等全局变量基本不存在空间异质性。     

城市住宅地价影响因素的定量识别与时空异质性——以武汉市为例

定量测度微观因素对住宅地价影响程度的时空分异特征是有效揭示住宅地价形成机制的重要条件。本文基于武汉市2007-2015年公开出让的住宅地价样点数据,采用广义可行最小二乘法(FGLS)修正的特征价格模型(Hedonic),测度微观影响因素对住宅地价影响程度的时空演变规律。结果显示,各要素对住宅地价影响程度的时序变化特征复杂,空间区域差异明显,具体而言：①2007-2015年间,武汉市住宅地价因素的影响程度从高到低依次为：容积率>医院>城市中心>城市副中心>地铁>公园。②在时序上,城市中心对住宅地价的影响程度呈增强趋势;城市副中心对住宅地价影响整体有所减弱;医院对住宅地价的作用程度呈指数式上涨;地铁对住宅地价增值作用主要发生在开通后;公园的影响持续下降;容积率的影响则在时序上先减后增,与住宅用地出让面积呈反向变动关系。③在空间上,在二环内中心区域,公园、容积率等对住宅地价影响程度较高;而在二环以外,与生活便利性相关的地铁、医院对住宅地价的作用更强;城市中心和副中心功能对住宅地价作用在空间上的互补效应明显。基于研究结果,强化景观用地等的宏观配置和保护力度,审慎确定容积率等是当前城市土地管理值得关注的问题。     

基于GWR的南京市住宅地价空间分异及演变

以南京市主城区为例,在空间自相关分析和蒙特卡罗检验的基础上,构建城市住宅地价地理加权回归模型,通过2003 年、2009 年住宅地价空间分异的对比,探讨不同影响因素对住宅地价影响的空间差异性及其随时间变化的特点,揭示住宅地价及其影响因素的空间变化关系,以促进地价的科学化管理。研究表明：① 随着影响因素数量不断增加以及合理均衡分布,区域差异性缩小,一般会减弱其对地价的影响。② 随着交通条件的不断完善,导致影响因素如CBD、主干道、公交等对地价的影响程度和范围发生变化。③ 随着时间推移,城市居民逐渐注重生活质量、居住品位的提高,公园绿地对地价的影响程度超过其他公用设施。