Spatial Autoregression

The paper by Cliff and Ord (1969) caused us to think more about the W matrix and the concept of spatial autoregression. This article reviews some applications of W , reinforcing the point that autoregression in time series is similar to, but not the same as, autoregression in spatial series.     

Investigating the Influence of Spatial and Temporal Granularities on Agent‐Based Modeling

Epidemic agent‐based models (ABMs) simulate individuals in artificial societies that are capable of movement, interaction, and transmitting disease among themselves. ABMs have been used to study the spread of disease at various spatial and temporal scales ranging from small communities to the world, over days, months, and years. The representations of space and time often vary between different epidemic ABMs and can be influenced by factors such as the size of a modeled population, computational requirements, population environments, and disease‐related data. The influence that the representations of space and time have on epidemic ABMs is difficult to assess. Here we show that the finest representations of space and time—termed spatial and temporal granularities (STGs)—in a parsimonious ABM affect speed, intensity, and spatial spread of a synthetic disease. Specifically, we found disease spread faster and more intensely as spatial granularity is coarsened, whereas disease spread slower and less intensely as temporal granularity is coarsened in a parsimonious ABM. Our study is the first to use the same epidemic ABM to examine the influence of STGs. Our results demonstrate that STGs influence ABM dynamics including early disease burnout and that an interrelationship exists between the coarsening of STGs and the speed and intensity at which disease spreads. Our parsimonious ABM is extended based on a structured community model and we found STGs also influence ABM dynamics in a more realistic context that includes hierarchical movement. Broadly, our study serves as a basis for further inquiry toward the influence of space–time representations on more realistic models that include multiscale mobility, routine movements (e.g., commuting), and heterogeneous population distributions.     

The effects of public capital on private sector performance in Turkish regional manufacturing industries1

This article investigates the impact of public capital formation on private manufacturing sector performance in the seven geographical regions of Turkey and in aggregate. A vector autoregression (VAR) model has been employed to estimate long run accumulated elasticities of private sector variables with respect to public capital for the time period 1980–2000. The results show that public capital affects private output positively in aggregate and in all regions apart from the Black Sea and Mediterranean regions. The results also reveal that only in the Marmara region, the impact is positive both on input and output. The public capital crowds in private sector inputs in some regions.     

Toy Story: Homophily,Transmission and the Use of Simple Simulation Models for Assessing Variability in the Archaeological Record

The interpretation of spatial and temporal patterns in the archaeological record remains a long-standing issue in the discipline. Amongst many methods and interpretations, modelling of ‘biased transmission’ has proved a successful strategy to tackle this problem. Here, we investigate a type of biased transmission, homophily, that is the tendency of individuals to associate and bond with similar others. In contrast to other social sciences, homophily remains underused in archaeology. In order to fill this gap, we develop six distinct variants of a well-established modelling framework borrowed from social science, Axelrod’s Cultural Dissemination Model. These so-called toy models are abstract models used for theory-building and aim at exploring the interplay between homophily and various factors (e.g. addition of spatial features such as mountains and coastlines, diffusion of innovations and population spread). The relevance and implications of each ‘toy model’ for archaeological reasoning are then discussed.     

An Approach to Urban Dynamics

Spatial cycles that are general to the urbanization process have been widely observed in the developed countries. These include (a) the decline of large agglomerations and the emergence of medium-sized municipalities, and (b) population decreases in the core and population increases in the suburban parts of the metropolitan area, followed later by contrasting increases in the core and decreases in the suburbs. In this paper, we formulate a mathematical model for such spatial cycles of the urbanization process as those mentioned above. Based on this model, we ascertain some general characteristics of these cycles of urban dynamics. From our findings, we can propose a long-term strategy for the urbanization process.     

On Geocomputational Determinants of Entropic Variations for Urban Dynamics Studies

Monitoring population characteristics and their patterns of spatial evolution are fundamental components for urban management and policy decision‐making. Societal issues such as health, transport, or crime are often explored using a range of models describing the urban dynamics of population attributes at specific scales that can be seen as complementary. Using and simulating data at different scales of aggregation asks for the need to analyze and compare spatiotemporal variations in order to better understand the model behaviors and emerging properties of the geosimulation. This article analyzes the uses of the entropy measure in the literature and constraining factors needed for its potential extension to explore the variations in geographic and time scales. In particular, the article discusses the need for a truly spatial entropy that takes into account the spatial contiguities of the observations usually aggregated within a zoning system of areal units. Two generic solutions are exposed for the various geometries and attribute structures used for census‐related analyses; they are based on existing measures for point data using (i) co‐occurrences of observations and (ii) discriminant ratios of distances between groups of observations. Their extensions to areal compositional data are articulated around their conceptual changes and geocomputational challenges. A revisited and new version of the entropy decomposition theorem, encompassing a spatiality concept semantically related to correlation, is also presented as efficiently reusing the constrained hierarchical zoning system of administrative units to enable discovery of emerging spatial pattern features from the geosimulation. A comparison of the results between the classical use of entropy and the spatial entropy framework devised shows the flexibility and added capabilities of the approach for new types of analyses, thus allowing new insight into studies of population dynamics.     

THE SPATIOTEMPORAL EVOLUTION OF U.S. CARBON DIOXIDE EMISSIONS: STYLIZED FACTS AND IMPLICATIONS FOR CLIMATE POLICY

We characterize the evolution of U.S. carbon dioxide (CO₂) emissions using an index number decomposition technique which partitions the 1963–2008 growth of states’ energy‐related CO₂ into changes in five driving factors: the emission intensity of energy use, the energy intensity of economic activity, the composition of states’ output, per capita income and population. Compositional change and declining energy intensity attenuate emissions growth, but their impacts are offset by increasing population and income. Despite absolute interstate divergence in both emissions and their precursors, states’ emission‐ and energy intensities—and ultimately, CO₂—appear to be stochastically converging. We assess the implications of these trends using a novel vector autoregression (VAR) emission forecasting technique based on our index numbers. The resulting emission projections are comparable to, but generally exceed, those forecast by the 2010 EIA Annual Energy Outlook.     

Agent-based simulation of the spatial evolution of the historical population in China

This paper presents an agent-based simulation (ABS) and cellular automata (CA) coupled model to simulate the spatial evolution of the population of China over the past 2000 years. In the model, agents are used to simulate individuals who live in a geographic environment represented by the CA. The choice to migrate is influenced by climate change, potential agricultural productivity change, and waves of mass migrations. Using the simulation, we can observe the spatial evolution of the population, as well as the shift of the population center of gravity, and we can analyze the driving forces of these changing spatial patterns.     

空间联系视角的沈阳市多中心城市结构研究

以沈阳市为研究案例,借鉴国际著名团队POLYNET的研究经验并加以改良,进行了空间联系视角的多中心城市结构的实证分析。分别从人口密度分布、就业-居住空间联系网络与企业空间组织网络三个维度来识别沈阳市的人口中心、就业中心与产业中心,综合分析这些"中心"得出四点结论:①城市核心区就业与居住中心构成十字交叉的内循环结构;②浑南新区次中心特征颇具雏形;③外围人口中心、就业中心与产业中心错位发展;④沈阳市呈现不均衡的多中心结构特征。     

Nonparametric Estimation of the Spatial Connectivity Matrix Using Spatial Panel Data

We use moments from the covariance matrix for spatial panel data to estimate the parameters of the spatial autoregression model, including the spatial connectivity matrix W. In the unrestricted spatial autoregression model, the parameters are underidentified by one when W is symmetric. We show that a special case exists in which W is asymmetric and its parameters are exactly identified. If the panel data are stationary and ergodic, spatially and temporally, the estimates of W and the spatial autoregression coefficients are consistent. Spatial panel data for house prices in Israel are used to illustrate this methodology. Los autores usan momentos de una matriz de covarianza para datos panel espaciales para estimar los parámetros del modelo de autoregresión espacial (spatial autoregressive model), incluyendo la matriz de conectividad (o de ponderación) espacial W. En el modelo de autoregresión espacial sin restricciones, los parámetros están sub‐identificados por un valor de uno en los casos que la matriz W es simétrica. Los autores demuestran que existe un caso especial en el cual la matriz W es asimétrica y sus parámetros tienen cálculo exacto. Si los datos panel son estacionarios y ergódicos, espacial y temporalmente, los estimados de W y el coeficiente de autoregresión espacial son consistentes. Para ilustrar la metodología propuesta, los autores usan datos‐panel espaciales de precios de vivienda en Israel. 本文通过采用空间面板数据的协方差矩阵对包含空间相关矩阵W的空间自回归模型进行参数的矩估计。在无约束空间自回归模型中,W是对称矩阵时,参数可由其估计得到。本文展示了一种W是对称矩阵且其参数能够被精确估计的特殊情况。如果面板数据在时间与空间特征上具有平稳性和遍历性,那么W和空间自回归参数的估计是一致的。最后,针对以色列住房价格的空间面板数据采用此方法进行实证研究。     

中国老年人口自评健康水平的空间分异及影响因素研究

基于2005年和2015年1%人口抽样调查资料以及2010年人口普查资料，采用ArcGIS分级制图和空间自相关分析等方法，刻画中国老年人口健康水平的时空格局及变化，并使用面板数据线性回归模型和Tobit回归模型识别影响老年人口健康水平空间分异的主要因素。结果表明：①中国老年人口健康水平存在明显的空间分异，老年人口健康水平高的地市主要分布在东南部省份、华北平原和新疆北部，老年人口健康水平低的地市主要集中在青藏高原及其周边地区；②十年间，老年人口健康水平的空间分异水平有所降低，老年人口健康水平中等的地市数量大幅增加；③老年人口健康水平呈现显著的空间正相关性，随着空间分异的缩小，空间集聚程度上升；老年人口健康水平的高-高类型区主要集中在东南部地区，低-低类型区主要分布在青藏高原及周边区域；④社会经济发展水平、人口结构、饮食习惯和自然地理因素是影响老年人口健康水平空间分异的重要因素，东部、中部和西部地区老年人口健康水平的影响因素存在显著的差异。     

Analysis of Dynamic Spatial Interaction Models by Means of Optimal Control

This paper deals with the design of general classes of dynamic spatial interaction models. On the basis of a general (well-behaved) multiperiod objective function and of a dynamic model representing the evolution of a spatial interaction system, an optimal control model is constructed. Particular attention is given to the equilibrium and stability conditions. It turns out that it is possible to identify steady-state solutions for a dynamic spatial interaction model. Furthermore, it can also be demonstrated that the entropy model is a specific case of this spatial interaction system. A simple illustration for urban dynamics is given as well.     

A Surface-Based Approach to Measuring Spatial Segregation

Quantitative indices of residential segregation have been with us for half a century, but suffer significant limitations. While useful for comparison among regions, summary indices fail to reveal spatial aspects of segregation. Such measures generally consider only the population mix within zones, not between them. Zone boundaries are treated as impenetrable barriers to interaction between population subgroups, so that measurement of segregation is constrained by the zoning system, which bears no necessary relation to interaction among population subgroups. A segregation measurement approach less constrained by the chosen zoning system, which enables visualization of segregation levels at the local scale and accounts for the spatial dimension of segregation, is required. We propose a kernel density estimation approach to model spatial aspects of segregation. This provides an explicitly geographical framework for modeling and visualizing local spatial segregation. The density estimation approach lends itself to development of an index of spatial segregation with the advantage of functional compatibility with the most widely used index of segregation (the dissimilarity index D ). We provide a short review of the literature on measuring segregation, briefly describe the kernel density estimation method, and illustrate how the method can be used for measuring segregation. Examples using a simulated landscape and two empirical cases in Washington, DC and Philadelphia, PA are presented.     

COINTEGRATION BETWEEN U.S. WHEAT MARKETS

ABSTRACT. Average monthly price data from twelve hinterland markets and the Houston port price for wheat are studied in a cointegration framework using the Engle-Granger "two-step" procedure and Johansen's maximum likelihood procedure. Out-of-sample forecasts from an error correction model are compared to those from a vector autoregression fit to levels and a univariate autoregression fit to first differences. This comparison suggests that modeling these (cointegrated) data as a levels vector autoregression, rather than as an error-correction process, results in significantly higher error bias, but lower error variance, at long horizons.     

Arms Racing in ‘Space’: Spatial Modelling of Military Spending around the World

This paper builds on previous research into determinants of military spending by examining global and local spatial effects. Other research has examined the effects of regional differences and neighbours' spending levels with standard econometric techniques. This paper uses spatial econometrics to gain a better understanding of the influence of location and distance on levels of defence spending. I find that a spatial lag specification provides much more robust evidence of arms racing and security dilemma dynamics than previous studies. These basic dynamics have been difficult to detect empirically without the context and nuance introduced by spatial modelling. The paper represents a first cut at the topic, but two specific findings emerge. First, globally there is positive spatial correlation (nearby states have similar levels of spending). This conforms to arms racing and security dilemma expectations. And, second, locally there is variation in the patterns of spatial clustering across broad international regions (e.g. Europe, Asia etc.). The second finding supports previous research suggesting important qualitative regional variation in patterns of defence spending and international conflict. The models also confirm the effects of political regime type and interstate war on defence spending, and are robust to the inclusion of a temporally lagged dependent variable.     

Models of settlement hierarchy based on partial evidence

The modelling of past settlement and landscape structure from incomplete evidence is a well-established archaeological agenda. This paper highlights a model of spatial interaction and settlement evolution that has long been popular in urban geography and which was first applied to model historical settlement hierarchies some twenty-five years ago, but whose use since then for archaeological purposes has been very limited. Via a case study from Bronze Age Crete, we extend the analytical range of this model by suggesting ways in which it can (a) remain effective in the presence of missing data, (b) be given a stronger grounding in the physical landscape, and/or (c) be used to consider the evolutionary trajectory of settlements and physical routes over time.     

不同模型在镇域空间相互作用中的应用与比较分析——以湖北省大冶市为例

镇域尺度的空间相互作用研究对促进城乡统筹和城镇体系规划具有重要意义。本文借鉴最新研究成果,从理论及实践层面对镇域空间相互作用研究进行了探索,提出一种基于空间可达性的辐射模型,并与传统重力模型进行比较分析。以大冶市16个乡镇为例进行实例研究,结果表明:1辐射模型与实测数据的相似度较高,可以更好地反映镇域空间相互作用的客观规律;2两种模拟方法在体现空间相互作用的方向性以及结构特征方面具有显著差异;3辐射模型中影响范围的引入以及空间可达性对区域异质性的体现是其与重力模型的主要区别。     

Spatial and temporal effects in Mexican direct elections for the chamber of deputies

By using data of the elections for the Chamber of Deputies of 1997 and 2000 in Mexico, we fit spatial autologistic models with temporal effects to test the significance of spatial and temporal effects on those elections. The binary variable of interest is the one that indicates a win of the National Action Party (PAN) or the alliance that it formed. By spatial effect, we refer to the fact that neighbouring constituencies present dependence on their electoral results. The temporal effect refers to the existence of dependence, for the same constituency, of the result of the election with the result of the previous election. The model that we used to test the significance of spatial and temporal effects is the spatial autologistic model with temporal effects for which estimation is complex and requires simulation techniques. By defining an urban constituency as one that contains at least one population center of 200,000 inhabitants or more, among our principal results, we find that, for the Mexican election of 2000, the spatial effect is significant only when neighbouring constituencies are both urban. For the election of 1997, the spatial effect is significant independent of the type of neighbouring constituencies. The temporal effect is significant on both elections.     

Gender, space, and the location changes of jobs and people: a spatial simultaneous equations analysis

This article summarizes a spatial econometric analysis of local population and employment growth in the Netherlands, with specific reference to impacts of gender and space. The simultaneous equations model used distinguishes between population- and gender-specific employment groups, and includes autoregressive and cross-regressive spatial lags to detect relations both within and among these groups. Spatial weights matrices reflecting different bands of travel times are used to calculate the spatial lags and to gauge the spatial nature of these relations. The empirical results show that although population–employment interaction is more localized for women's employment, no gender difference exists in the direction of interaction. Employment growth for both men and women is more influenced by population growth than vice versa. The interaction within employment groups is even more important than population growth. Women's, and especially men's, local employment growth mostly benefits from the same employment growth in neighboring locations. Finally, interaction between these groups is practically absent, although men's employment growth may have a negative impact on women's employment growth within small geographic areas. In summary, the results confirm the crucial roles of gender and space, and offer important insights into possible relations within and among subgroups of jobs and people.     

THE IMPACT OF CRIME ON APARTMENT PRICES: EVIDENCE FROM STOCKHOLM,SWEDEN

This study uses data of about 9,000 apartment sales in Stockholm, Sweden, to assess the impact of crime on property prices. The study employs hedonic pricing modelling to estimate the impact of crime controlling for other factors (property and neighbourhood characteristics). Geographic Information System (GIS) is used to combine apartment sales by coordinates with offences, land use characteristics and demographic data of the population. The novelty of this research is threefold. First, it explores a set of land use attributes created by spatial techniques in GIS in combination with detailed geographical data in hedonic pricing modelling. Second, the effect of crime in neighbouring zones at one place can be measured by incorporating spatial lagged variables of offence rates into the model. Third, the study provides evidence of the impact of crime on housing prices in a capital city of a traditional welfare state, information otherwise lacking in the international literature. Our results indicate that apartment prices in a specific area are strongly affected by crime in its neighbouring zones, regardless of crime type. When offences were broken down by types, residential burglary, theft, vandalism, assault and robbery individually had a significant negative effect on property values. However, for residential burglary such an effect is not homogenous across space, and apartment prices in central areas are often less discounted by being exposed to crime than those in the city's outskirts.