Detrended fluctuation analysis of daily atmospheric surface temperature records in Atlantic Canada

The presence and properties of long‐range correlations in temperature data reflect interactions among climate components; therefore, the quantitative characterization of scaling aspects of temperature patterns is important to climate research and can serve as an effective constituent of tests for climate models. The article presents the results of a study using multiscale characterization of daily atmospheric surface temperature patterns in Atlantic Canada. Important influences in this region are exerted from the west (the Pacific Ocean), the south (the Gulf of Mexico) and the north (the Arctic), while a significant easterly impact is due to the Atlantic Ocean; the corresponding processes involve a wide range of spatial and temporal scales. The objective of the present study of long‐term temperature recordings was to evaluate the scaling properties produced in this geographical context. The data consist of homogenized daily atmospheric temperature time series recorded in stations from Atlantic Canada over a time interval of more than 100 years. Detrended fluctuation analysis (DFA) was applied both to maximum and minimum temperature records. The atmospheric temperature pattern produced by the interplay of factors of different strengths and dominating various time‐space scales was found to be characterized by consistent scaling properties, expressed over time intervals ranging from months to decades. Higher values of DFA scaling exponents were obtained for minimum temperature compared to maximum temperature records. Site‐specific properties include stronger pattern persistence—higher DFA exponents—for oceanic than for coastal locations; persistence tends to decrease with increasing distance from the coast for distances up to 10 kilometres. Scaling exponents tend to increase with decreasing difference between average minimum and maximum temperature, which may be relevant for the assessment of future changes in pattern variability if climate change involves modified contrasts between minimum and maximum temperature values.     

Dimensions of Spatial Price Dispersion Before and After the Crash of 1929

The spatial and temporal patterns of prices are analyzed over the 1919–40 period. Arguments are made for the distinctiveness of different economic episodes, including the crash of 1929, in terms of their local price effects. Nevertheless, it is also shown that the variability between cities and between components of their price series declined through the early 1940s. Alternative conceptions of spatial price systems are introduced, and due regard is also given to the structure and interpretation of city CPI data. Implications are drawn regarding the proper design of a theory that would explain the observed patterns of spatial price dispersion. This larger project is left, however, to another time.     

Spatial and Temporal Variation in Mesopotamian Agricultural Practices in the Khabur Basin,Syrian Jazira

While archaeologists have long recognized the value of regional analysis to define economic systems, relatively few archaeological data sets—principally settlement patterns, ceramics and lithics—have been used to assess regional-scale spatial variation and temporal change. As with other archaeological data sets, resolution of archaeobotanical data at a regional scale poses some formidable challenges. A new approach in the Near East uses archaeobotanical remains from multiple sites. The data have been drawn from midden assemblages that exhibit high variability between assemblages, requiring the use of random effects logistic regression models that can accommodate high variability. Our approach detects changes over time and over geographical region and tests the statistical significance of these changes. Results show a significant rise in crop-processing wastes, most probably from a specialized focus on barley processing at settlement and storage sites during the 3rd millenniumbc (Ninevite 5 and Early Jazira Periods). This shift to a greater representation of barley-processing by-products represented in middens can most probably be linked with an emerging specialization in pastoral production and re-settlement in arid regions of the northern Mesopotamian steppe in the 3rd millennium bc.     

Spatial Analysis of the Factors Contributing to the Relationship between the Transient,Meridional Eddy Sensible,and Latent Heat Flux in the Southern Hemisphere

In this paper principal component analysis (PCA) and singular value decomposition (SVD) are used to define the importance of the variables contributing to the relationship between the transient latent and sensible heat fluxes and to show their temporal and spatial variation. SVD is offered as an alternative means of isolating spatial and temporal structures in data with the advantage that it can depict simultaneous space‐time variations that are aggregates of the results produced by PCA. Both methods of analysis produced two very important uncorrelated modes of variability in January and July, indicating that the transient heat fluxes are influenced by few controlling factors. We suggest that these modes of variability represent the influences of the meridional temperature gradient, atmospheric moisture, and activity within the source and sink regions of the transient heat fluxes. The physical relationships between the heat fluxes that appear to represented by the statistical modes of variability are discussed.     

Spatial‐temporal patterns of snow cover in western Canada

Snow cover is often measured as snow‐water equivalent (SWE), which refers to the amount of water stored in a snow pack that would be available upon melting. Snow cover and SWE represent a source of local snow‐melt release, and are sensitive to regional and global atmospheric circulation, and changes in climate. Monitoring SWE using satellite‐based passive microwave radiometry has provided nearly three decades of continuous data for North America. The availability of spatially and temporally extensive SWE data enables a better understanding of the nature of space‐time trends in snow cover, changes in these trends and linking these trends to underlying landscape and terrain characteristics. To address these interests, we quantify the spatial pattern of SWE by applying a local measure of spatial autocorrelation to 25 years of mean February SWE derived from passive microwave retrievals. Using a method for characterizing the temporal trends in the spatial pattern of SWE, temporal trends and variability in spatial autocorrelation are quantified. Results indicate that within the Canadian Prairies, extreme values of SWE are becoming more spatially coherent, with potential impacts on water availability, and hazards such as flooding. These results also highlight the need for Canadian ecological management units that consider winter conditions.     

Spatial/temporal mismatch: a conflation protocol for Canada Census spatial files

The Canada census is one of the chief sources of demographic and socio-economic data for researchers in this country. Census variables are linked to geography files that allow researchers using geographic information systems (GIS) to view and analyze spatial data. Some of the most useful analysis, however, is based on changes in attribute values over time and space. Analysis of spatio- temporal events such as shifting migration patterns or changes in the distribution of health status permits a more dimensioned perspective than the viewing of static spatial phenomena. The analysis of spatio-temporal phenomena is limited by major changes in the spatial framework (e.g., location of road networks and other spatial entities) between national censuses. This paper addresses this limitation by (i) illustrating the extent of spatial mismatch between the 1996 and the 2001 census; (ii) examining attempts to rectify this problem in other jurisdictions and (iii) presenting a 'made-in-Canada' solution for conflation of census geometries. We believe that this solution will enhance the ability of Canadian researchers to describe and analyze socio-economic, health and demographic shifts across time and space. The research is supported by an ftp site for downloading the census geography rectification software presented in this paper.     

Duration Modeling of Spatial Point Patterns

This paper attempts to further the research by Odland and Ellis (1992) in applying event history methodology to the analysis of spatial point patterns (that is, event patterns). Its empirical focus is the event pattern derived from the adoption of an agricultural innovation, the Harvestore, in southern Ontario, Canada, from 1963 to 1986. Event history analysis involves the use of discrete-state, continuous-time stochastic models to investigate a temporal longitudinal record on discrete variables. Event history models are usually concerned with durations of time between events and the effects of intertemporal time dependencies on future event occurrences. As such, they are often referred to as duration models. Many of the methods used in event history analysis allow the use of other nonnegative interval measurements in place of standard temporal intervals to investigate a series of events. In particular, spatial intervals (or durations) of distances between events may also be accommodated by event history models. Our analysis extends the previous research of Odland and Ellis (1992) by using a wider range of parametric models to explore duration dependence, investigating the role of spatial censoring, and using a more extensive set of explanatory variables. In addition, simulation experiments and graphical tests are used to evaluate the empirical event pattern against one generated from Complete Spatial Randomness. Results indicate that the event pattern formed by the Harvestore adopter farms is clustered (that is, is described by positive duration dependency), the sales agent is a significant factor in the distribution of adopters, and that contrasting results are obtained from the analysis using censored data versus uncensored data.     

Spatial-Domain Chaos in Landscapes

It has been speculated that deterministic chaos may be responsible for the spatial complexity commonly observed in biophysical landscapes. However, deterministic chaos is a temporally defined concept and has been rigorously investigated only in the temporal domain. It is shown here, using a simple hillslope regolith cover model as an example, that temporal chaos in the presence of even minor spatial anisotropy will result in a spatially chaotic pattern. It is also shown that behavior of spatially chaotic landscape systems can be deduced, at least in theory, from spatial patterns of a single landscape component or variable. Because stochastic complexity is likely to exist in landscapes in addition to any deterministic complexity, detecting and analyzing chaotic behavior in landscapes is difficult. Progress in efforts to examine chaos in the spatial domain and to isolate deterministic and stochastic complexity may be the key to developing a theory of landscape evolution that can reconcile the simultaneous presence of apparently random, irregular variability and orderly, regular patterns.     

New insights into the neolithisation process in southwest Europe according to spatial density analysis from calibrated radiocarbon dates

The agricultural way of life spreads throughout Europe via two main routes: the Danube corridor and the Mediterranean basin. Current archaeological literature describes the arrival to the Western Mediterranean as a rapid process which involves both demic and cultural models, and in this regard, the dispersal movement has been investigated using mathematical models, where the key factors are time and space. In this work, we have created a compilation of all available radiocarbon dates for the whole of Iberia, in order to draw a chronological series of maps to illustrate temporal and spatial patterns in the neolithisation process. The maps were prepared by calculating the calibrated 14C date probability density curves, as a proxy to show the spatial dynamics of the last hunter-gatherers and first farmers. Several scholars have pointed out problems linked with the variability of samples, such as the overrepresentation of some sites, the degree of regional research, the nature of the dated samples and above all the archaeological context, but we are confident that the selected dates, after applying some filters and statistical protocols, constitute a good way to approach settlement spatial patterns in Iberia at the time of the neolithisation process.     

Energy input and spectral variations of precipitated particles during three nights of the Energy Budget Campaign

Observations were carried out during the Energy Budget Campaign with the aim of measuring keV particle precipitation in the ionosphere over a region covering the rocket range. These measurements are important as particle precipitation constitutes one of the major energy inputs into the ionosphere during an auroral substorm. Estimates of electron precipitation have been made in three different regions of the energy spectrum. Two optical emissions were measured that are proportional to electron precipitation of a few hundred eV and a few keV, respectively, and cosmic noise absorption related to electrons of a few tens of keV. The relative variation between the signals may be interpreted as changes of the energy spectrum of the particles. Measurements from the ground have been compared with satellite measurements to obtain the relation between the two methods. The ground-based observations, which are continuous in time and cover a large area, may be used to separate the spatial and temporal variations and to give the large scale substorm frame of reference for the rocket data. The results may also be used in the interpretation of other measurements on board rockets where a knowledge of the precipitated particles is required.     

Spatial Analysis in Physical Geography and the Challenge of Deterministic Uncertainty

The application of complex and nonlinear dynamical systems (NDS) theory in physical geography and geosciences has proceeded through several stages, and has recently entered a phase where field-testable hypotheses and historical or mechanistic explanations are being generated. However, there are some fundamental challenges. It seems clear that chaos and dynamical self-organization are present, and may be common in earth surface systems, and that these phenomena have spatial manifestations in the landscape. However, NDS theory and methods have been formulated primarily in the temporal domain and are typically ill-suited to real-world spatial data. Spatial analytical methods are not generally capable of distinguishing deterministic complexity and uncertainty from noise. Thus, the detection of the signals of complex deterministic dynamics in real landscapes and spatial data is a major challenge. Entropy-based methods of spatial analysis can be directly linked to nonlinear dynamics, and are at present the best available method to approach this problem. However, there is evidence in the spatial analysis literature suggesting that development of techniques to detect deterministic uncertainty is possible. Pending such a break-through, three general approaches are described, based on spatial analysis of chronosequences, the characteriziation of changes in spatial structure over time, and the spatial-domain testing of specific hypotheses relevant to deterministic uncertainty. Current trends generally suggest a shift in mathematical modeling and spatial analysis in physical geography away from traditional determinism toward approaches that incorporate locational, historical, and scale contingency.     

A SPATIO‐TEMPORAL KERNEL METHOD FOR MAPPING CHANGES IN PREHISTORIC LAND‐USE PATTERNS

Archaeologists are accustomed to considering both the spatial distributions of sites and the temporal distributions of dates as means of analysing the dynamics of prehistoric societies. However, spatial and temporal approaches have thus far remained largely separate, rather than being combined within a single, unified framework. A formal methodology is outlined that combines univariate kernel density estimation based on radiocarbon dates with bivariate kernel density estimation based on spatial site coordinates; the approach allows archaeologists to arrive at reconstructed land‐use distributions through time that not only correct for the problematic issue of site contemporaneity, but also reflect the continuous nature of the archaeological record. The model is implemented using as a data set a series of sites from the Mesolithic of Atlantic Iberia; the results demonstrate that it is capable of providing key insights into changing patterns of land use that are not apparent from either the temporal or the spatial perspective alone.     

The Changing Face of Neolithic and Bronze Age Ireland: A Big Data Approach to the Settlement and Burial Records

This paper synthesizes and discusses the spatial and temporal patterns of archaeological sites in Ireland, spanning the Neolithic period and the Bronze Age transition (4300–1900 cal BC), in order to explore the timing and implications of the main changes that occurred in the archaeological record of that period. Large amounts of new data are sourced from unpublished developer-led excavations and combined with national archives, published excavations and online databases. Bayesian radiocarbon models and context- and sample-sensitive summed radiocarbon probabilities are used to examine the dataset. The study captures the scale and timing of the initial expansion of Early Neolithic settlement and the ensuing attenuation of all such activity—an apparent boom-and-bust cycle. The Late Neolithic and Chalcolithic periods are characterised by a resurgence and diversification of activity. Contextualisation and spatial analysis of radiocarbon data reveals finer-scale patterning than is usually possible with summed-probability approaches: the boom-and-bust models of prehistoric populations may, in fact, be a misinterpretation of more subtle demographic changes occurring at the same time as cultural change and attendant differences in the archaeological record.     

2000—2009年广东省入境游客时空分布格局及其变化研究

依据2000—2009年广东省入境旅游的相关数据,从时间序列和空间尺度两个方面对广东省入境游客的年际变化、时间分布、空间集聚性和空间格局演变进行分析。结果表明:(1)除个别地级市外,广东省大部分地级市入境游客到访率年际变化稳定,年际集中指数较小,时间分布均匀;(2)入境游客空间分布地区差异,呈明显的集中型格局,主要集中于珠三角地区,且有进一步向其集中的态势;(3)入境游客空间分布市域差异,呈现从以广州、深圳为双中心,向四周辐散的"中心双核辐射式结构"到以深圳、广州、珠海、东莞以及惠州为多中心,向四周辐散的"中心多核辐射式结构"过度趋势,空间集聚性指数下降,入境游客空间分布趋于分散均匀,市域差异有缩小的趋势。     

Agricultural potential and actualized development in Hawai’i: an airborne LiDAR survey of the leeward Kohala field system (Hawai’i Island)

Archaeological investigations of Hawaiian agriculture have relied on relatively coarse-grained data to investigate archipelago-wide processes, or on fine-grained data to examine patterning within localized zones of agricultural production. These trade-offs between spatial coverage and data resolution have inhibited understanding of both spatial patterns and temporal trends. Our analysis of 173 km² of high-resolution airborne Light Detection and Ranging (LiDAR) data for leeward Kohala, Hawai’i Island identifies spatial and temporal patterning in regional agricultural development. Differential densities of alignments suggest variable levels of agricultural intensity. Agricultural processes of expansion, segmentation, and intensification can also be discriminated, with distinct zones of the field system having undergone different mixes of development. Areas within the field system with moderate to high levels of both average production and variability in production (determined using a climate-driven productivity model) were utilized relatively early in a highly intensified manner; these areas often underwent processes of segmentation and intensification. Less productive areas were developed later and exhibit evidence of expansion with lower amounts of segmentation and intensification, at set levels of intensity. The spatial and temporal variability in agricultural activities was influenced by the diverse environmental conditions across the landscape as well as variation in cultivars and cultivation techniques. Combining the high-resolution LiDAR data from a large area with potential productivity modeling allows for a more fine-grained understanding of agricultural development in this region of the Hawaiian archipelago.     

Spatial and Scale‐Dependent Controls on North American Pan‐Arctic Minimum River Discharge

Spatial patterns of minimum monthly river discharge in the North American Pan‐Arctic and its potential controls are explored with geographically weighted regression (GWR). Minimum discharge is indicative of soil water conditions; therefore, understanding spatial variability of its controls may provide insights into patterns of hydrologic change. Here, GWR models are applied to determine a suitable combination of independent variables selected from a set of eight variables. A model specification with annual mean river discharge, temperature at time of minimum discharge, and biome describes well the spatial patterns in minimum discharge. However, minimum discharge in larger watersheds is influenced more by temperature and biome distributions than it is in small basins, suggesting that scale is critical for understanding minimum river discharge. This study is the first to apply GWR to explore spatial variation in Pan‐Arctic hydrology. Factores de control espaciales y dependientes de escala en las descargas fluviales mínimas de ríos Pan‐Articos en Norteamérica. El artículo explora los patrones espaciales de caudales fluviales mínimos mensuales la región pan‐ártica de Norteamérica y sus posibles factores de control haciendo uso de una regresión ponderada geográficamente (geographically weigted regression‐GWR). Los caudales mínimos son indicadores de las condiciones del agua en el suelo, y por lo tanto el entendimiento de la variabilidad espacial de los factores que los controlan puede ayudar a comprender los patrones de cambio hidrológico. En el presente estudio, varios modelos de tipo GWR son aplicados para determinar una combinación adecuada de variables independientes seleccionadas a partir de un conjunto de ocho variables. El modelo que utiliza la media anual media de descarga fluvial, la temperatura en el momento de caudal mínimo, y el bioma, proporciona una buena descripción de los patrones espaciales en la descarga mínima. Sin embargo, en las cuencas hidrográficas grandes, la descarga mínima está más influenciada por la temperatura y la distribución de los biomas que en el caso de cuencas más pequeñas, lo que sugiere que la escala es fundamental para entender la descarga mínima fluvial. Este estudio es el primero en aplicar GWR para comprender la variación espacial en la hidrología de la región pan‐ártica. 基于GWR（地理加权回归模型）对北美泛北极地区月份最小河流流量的空间模式和潜在控制进行研究。最小流量暗示水土条件;因此,理解空间分异及控制可深刻理解水文变化的模式。GWR可从8个变量中提取一组独立变量的适当组合。通过年均河流流量、最小流量时的温度和生物群落,来描述最小下泄流量的空间格局。在大范围流域中,最小流量受到温度和生物群落分布的影响大于在小规模的流域,揭示出在河流最小流量分析中尺度是非常重要的。本文首次将GWR应用于泛北极水文空间异质性分析。     

基于DPSIR模型的城市人居环境韧性评价——以长三角城市群为例

本文将韧性原理引入到人居环境中,首次对城市人居环境韧性进行定量研究.基于DPSIR模型构建城市人居环境韧性评价指标体系,综合运用熵值法、GIS空间分析法和地理探测器技术,以2006、2010、2014、2018年为研究时间节点,对长三角城市群26个城市的人居环境韧性时空演化特征及影响因素进行分析.研究发现:①长三角城市...     

Spatiotemporal Modeling of Correlated Small‐Area Outcomes: Analyzing the Shared and Type‐Specific Patterns of Crime and Disorder

This research applies a Bayesian multivariate modeling approach to analyze the spatiotemporal patterns of physical disorder, social disorder, property crime, and violent crime at the small‐area scale. Despite crime and disorder exhibiting similar spatiotemporal patterns, as hypothesized by broken windows and collective efficacy theories, past studies often analyze a single outcome and overlook the correlation structures between multiple crime and disorder types. Accounting for five covariates, the best‐fitting model partitions the residual risk of each crime and disorder type into one spatial shared component, one temporal shared component, and type‐specific spatial, temporal, and space–time components. The shared components capture the underlying spatial pattern and time trend common to all types of crime and disorder. Results show that population size, residential mobility, and the central business district are positively associated with all outcomes. The spatial shared component is found to explain the largest proportion of residual variability for all types of crime and disorder. Spatiotemporal hotspots of crime and disorder are examined to contextualize broken windows theory. Applications of multivariate spatiotemporal modeling with shared components to ecological crime theories and crime prevention policy are discussed.     

Multiscalar Settlement Ecology Study of Piedmont Village Tradition Communities,A.D. 1000–1600

This research analyzes the spatial patterning of settlement sites in relation to landscape features to determine the factors that influenced settlement location choices for Late Precontact (A.D. 1000–1600) Piedmont Village Tradition (PVT) communities in the Yadkin, Dan, Haw, and Eno river valleys of the Piedmont Southeast. We employ geographic information systems to estimate characteristics of past landscapes, nearest neighbor analysis to describe basic settlement patterns, and discriminant function analysis to determine spatial correlations between settlements and landscape features. We examine the data on three scales and also assess potential changes over time. Results indicate that settlement location choices were broadly similar on the regional scale, but specific influences varied between and within valleys and over time. When examined with current archaeological, ethnohistoric, and linguistic information, the results suggest that PVT communities engaged in regional interaction networks in highly variable ways and that the relationship between subsistence and settlement varied according to settlement size. Using these results, we explore the roles PVT communities played in the formation and maintenance of natural and cultural landscapes in the Late Precontact Southeast.     

SPACE–TIME MEASURES OF DEMAND FOR SERVICE: BRIDGING LOCATION MODELLING AND ACCESSIBILITY STUDIES THROUGH A TIME‐GEOGRAPHIC FRAMEWORK

Demand for service in location modelling is often evaluated based on the spatial proximity of fixed and static reference locations of demand (e.g. home) to a facility, which ignores person‐specific activity–travel patterns and the temporal changes in demand for service throughout the day. To address these limitations, this study draws upon recent developments in space–time measures of individual accessibility to explore the spatial and temporal structures of demand by considering individuals' space–time constraints and impact of existing urban structures. Based on a time‐geographic framework, eight space–time demand measures were developed and compared with three conventional location‐based demand measures for 12 hospitals through an empirical study conducted in Columbus, Ohio. The results show that geographic proximity between clients' home and facilities may not be an effective indicator for service demand, and conventional demand measures tend to underestimate potential demand for service in most situations. The study concludes that space–time demand measures that take into account people's activity‐travel patterns in space–time would lead to better estimation of demand for service in most cases.