A space-time analysis of knowledge production

Regional growth models often emphasize the importance of research and development activities leading to technological progress. The role of knowledge production and spatiotemporal spillover effects is investigated using a space-time panel data set covering 49 US states over the period 1994–2005. The aim is to test for the existence of regional knowledge spillovers in the context of a space-time dynamic suggested by the knowledge production function. A space-time specification is set forth that can be applied to panel data models that include random effects. We compare alternative models that have been proposed in the panel data literature to provide a better understanding of how new ideas diffuse across space and time. The results indicate that the space-time panel data set is consistent with the presence of strong spatiotemporal regional spillovers of knowledge. The empirical findings are interpreted in light of the existing theoretical and empirical literature on endogenous growth.     

The ecological fallacy in a time series context: evidence from Spanish regional unemployment rates

The ecological fallacy (EF) is a common problem regional scientists have to deal with when using aggregated data in their analyses. Although there is a wide number of studies considering different aspects of this problem, little attention has been paid to the potential negative effects of the EF in a time series context. Using Spanish regional unemployment data, this paper shows that EF effects are not only observed at the cross-section level, but also in a time series framework. The empirical evidence obtained shows that analytical regional configurations are the least susceptible to time effects relative to both normative and random regional configurations, while normative configurations are an improvement over random ones.

The impact of spatial elements on the forecasting of Spanish labour series

In this paper, we analyse the ability of a dynamic spatial panel data model without explanatory variables to explain a variable of interest, in this case employment in the fifty Spanish provinces. The best model is a dynamic fixed effect with a spatial lag structure in an equation estimated through the unconditional ML procedure. Predictions derived from this selected model are compared with those derived from fifty seasonal ARIMA models that also treat outlier observations. The results indicate that forecasts derived from a single estimated spatial panel data model are as accurate as those derived from the estimation of fifty seasonal ARIMA models. This shows that spatial panel data models play an important role in forecasting.     

Monitoring growth of built-up areas in indo-gangetic plain using multi-sensor remote sensing data

This paper provides an approach for rapid and accurate estimation of built-up areas on a per pixel-basis using a integration of two coarse spatial resolution remote sensing data namely DMSP-OLS and MODIS NDVI. The DMSP-OLS data due to its free availability, high temporal resolution and wide swath was used for regional level mapping of built-up areas. However, due to its low radiometric resolution, the built-up areas cannot be estimated accurately from the DMSP-OLS data. In present study, the DMSP-OLS data was combined with MODIS NDVI data to develop an Human Settlement Index (HSI) image, which estimated the fraction of built-up area on a per pixel basis. The resultant HSI image conveys more information than both the individual datasets. These temporal HSI images were then used for monitoring urban growth in Indo-Gangetic plains during the 2001–2007 time period. Thus, the present research can be very useful for regional level monitoring of built-up areas from coarse resolution data within limited time and minimal cost.     

Spatial error correction and cointegration in nonstationary panel data: regional house prices in Israel

We “spatialize” residual-based panel cointegration tests for nonstationary spatial panel data in terms of a spatial error correction model (SpECM). Local panel cointegration arises when the data are cointegrated within spatial units but not between them. Spatial panel cointegration arises when the data are cointegrated through spatial lags between spatial units but not within them. Global panel cointegration arises when the data are cointegrated both within and between spatial units. Spatial error correction arises when error correction occurs within and between spatial units. We use nonstationary spatial panel data on the housing market in Israel to illustrate the methodology. We show that regional house prices in Israel are globally cointegrated in the long run and there is evidence of spatial error correction in the short run.     

From locational fundamentals to increasing returns: the spatial concentration of population in Spain, 1787–2000

Why is population not evenly distributed throughout a country’s territory? This paper focuses on the case of Spain, in order to empirically test two complementary theoretical explanations: (a) first nature advantages or locational fundamentals; and (b) second nature advantages or increasing returns. We estimate population density and population growth equations for the case of Spain between 1787 and 2000. Our results suggest that locational fundamentals explain the distribution of population prior to industrialization and that industrialization reinforced the pre-existing regional population disparities, especially as the share of increasing-returns sectors in the Spanish economy became significant. Finally, we perform an ANOVA analysis which shows that although in the pre-industrial economy first nature advantages were the most important in explaining the growth in provincial population densities, these were progressively superseded by the influence of first via second nature effects.     

基于DMSP/OLS夜光遥感影像的城市扩张政策驱动因素分析

城市扩张的速度和方向已成为社会关注的焦点,利用夜光遥感进行城市扩张驱动力成因分析是近年来的研究热点。本文采用DMSP卫星所获取的年平均中国区域夜光影像数据,首先对1992—2012年共21年的时序夜光影像进行相对辐射定标;其次通过经验阈值法进行城市建成区面积提取,并通过Landsat影像进行精度验证;最后引入计量经济模型,以地级市主政官员政治周期为解释变量,对全国1992—2012年地级市扩张的政策驱动力因素进行了归因分析。通过本文的分析可知,地级市主政官员政治周期的更替对城市扩张方向存在显著影响。     

Modelling urban growth in the Indo-Gangetic plain using nighttime OLS data and cellular automata

The present study demonstrates the applicability of the Operational Linescan System (OLS) sensor in modelling urban growth at regional level. The nighttime OLS data provides an easy, inexpensive way to map urban areas at a regional scale, requiring a very small volume of data. A cellular automata (CA) model was developed for simulating urban growth in the Indo-Gangetic plain; using OLS data derived maps as input. In the proposed CA model, urban growth was expressed in terms of causative factors like economy, topography, accessibility and urban infrastructure. The model was calibrated and validated based on OLS data of year 2003 and 2008 respectively using spatial metrics measures and subsequently the urban growth was predicted for the year 2020. The model predicted high urban growth in North Western part of the study area, in south eastern part growth would be concentrated around two cities, Kolkata and Howrah. While in the middle portion of the study area, i.e., Jharkhand, Bihar and Eastern Uttar Pradesh, urban growth has been predicted in form of clusters, mostly around the present big cities. These results will not only provide an input to urban planning but can also be utilized in hydrological and ecological modelling which require an estimate of future built up areas especially at regional level.     

A spatial econometric panel data examination of endogenous versus exogenous interaction in Chinese province-level patenting

We examine the provincial-level relationship between domestic Chinese intellectual property (IP) and knowledge stocks using a space–time panel model and data set covering monthly patent activity over the period 2002–2010. The goal of the modeling exercise is to explore the elasticity response of IP to knowledge stocks classified by type of creator (universities and research institutes, enterprises, and individuals). A focus is on spatial and time dependence in the relationship between knowledge stocks and IP, which implies spatial spillovers and diffusion over time. Many past studies of regional knowledge production have focused on patent applications as a proxy for regional output from the knowledge production process. However, this ignores the distinction between patent applications and patents granted, with the latter reflecting a decision and ability to convert knowledge produced into IP. This study differs in its focus on the regional relation between IP and knowledge stocks and the space–time dynamics of these. Using patents granted as a proxy for IP, and past patent applications as a proxy for regional knowledge stocks, allows us to explore the implied quality of knowledge production by various types of creators. Because Chinese patent applications have grown by 22 %, questions have been raised about the quantity versus quality of these applications. Our findings shed light on this issue.     

区域作物生长过程的遥感提取方法

提出利用时序NDVI数据提取作物生长过程方法。遥感数据在采集过程中受云、大气因子的影响 ,以及混合像元问题 ,造成时序植被指数值变得没有规律 ,对比性不强。采用基于最小二次方拟合的谐函数分析方法 ,依据作物轮作规律和生长周期性特征 ,用主要频率的正弦、余弦谐函数重建时序图像 ,去除了影像中云污染的影响。以中国的旱地为例 ,考虑到像元内旱地对NDVI值的贡献率 ,计算区域内旱地像元加权平均值来反映其作物生长过程。同时与区域所有像元的平均值、旱地平均值等统计方法的结果进行对比分析 ,表明区域内旱地的加权平均值能够削弱旱地比例和地域间的差异 ,突出耕地上作物的生长过程特征。通过与地面实测数据分析 ,平滑前后的作物生长过程与叶面积指数相关性增加 5 %— 11% ,采用区域加权平均的方法得到的作物生长过程 ,比旱地平均和NDVI平均的结果与叶面积指数的相关性增加 14 %— 17%。     

Monitoring winter wheat growth in North China by combining a crop model and remote sensing data

Both of crop growth simulation models and remote sensing method have a high potential in crop growth monitoring and yield prediction. However, crop models have limitations in regional application and remote sensing in describing the growth process. Therefore, many researchers try to combine those two approaches for estimating the regional crop yields. In this paper, the WOFOST model was adjusted and regionalized for winter wheat in North China and coupled through the LAI to the SAIL–PROSPECT model in order to simulate soil adjusted vegetation index (SAVI). Using the optimization software (FSEOPT), the crop model was then re-initialized by minimizing the differences between simulated and synthesized SAVI from remote sensing data to monitor winter wheat growth at the potential production level. Initial conditions, which strongly impact phenological development and growth, and which are hardly known at the regional scale (such as emergence date or biomass at turn-green stage), were chosen to be re-initialized. It was shown that re-initializing emergence date by using remote sensing data brought simulated anthesis and maturity date closer to measured values than without remote sensing data. Also the re-initialization of regional biomass weight at turn-green stage led that the spatial distribution of simulated weight of storage organ was more consistent to official yields. This approach has some potential to aid in scaling local simulation of crop phenological development and growth to the regional scale but requires further validation.     

Remote sensing of human beings – a perspective from nighttime light

City lights, fishing boats, and oil fields are the major sources of nighttime lights, therefore the nighttime light images provide a unique source to map human beings and their activities from outer space. While most of the scholars focused on application of nighttime light remote sensing in urbanization and regional development, the actual fields are much wider. This paper summarized the applications of nighttime light remote sensing into fields such as the estimation of socioeconomic parameters, monitoring urbanization, evaluation of important events, analyzing light pollution, fishery, etc. For estimation of socioeconomic parameters, the most promising progress is that Gross Domestic Product and its growth rate have been estimated with statistical data and nighttime light data using econometric models. For monitoring urbanization, urban area and its dynamics can be extracted using different classification methods, and spatial analysis has been employed to map urban agglomeration. As sharp changes of nighttime light are associated with important socioeconomic events, the images have been used to evaluate humanitarian disasters, especially in the current Syrian and Iraqi wars. Light pollution is another hotspot of nighttime light application, as the night light is related to some diseases and abnormal behavior of animals, and the nighttime light images can provide light pollution information on large scales so that it is much easier to analyze the effects of light pollutions. In each field, we listed typical cases of the applications. At last, future studies of nighttime light remote sensing have been predicted.     

Nighttime light data-based analysis of spatiotemporal patterns evolution and driving forces of economic development in China at prefecture level (1992–2020)

Intercity lighting data are an important resource for studying spatial and temporal patterns in regional urban development as an indicator of the intensity of urban social and economic activity. Understanding the evolutionary characteristics of the spatial pattern of regional economic development can support decision-making in regional economic coordination and sustainable development strategies. Based on a long time series of nighttime lighting data from 1992 to 2020, this study used the Theil index, Markoff transfer matrix, spatial autocorrelation, and spatial regression to analyze spatiotemporal evolutionary characteristics and drivers of urban economic development in China. The study found that from 1992 to 2020, China's economic development hot spots have been concentrated in highly developed urban agglomerations namely the Beijing–Tianjin–Hebei region, Shandong Peninsula, Yangtze River Delta, and Pearl River Delta. Cold spots were mainly concentrated in the central-west and southwest of the country. The economic growth rate shows an opposite spatial pattern, which demonstrates the effectiveness of the national coordinated development strategy for regions. The Theil index for urban economic development in China shows an overall downward trend, and the overall economic disparity is mainly due to the relatively low economic development of Tibet, Xinjiang, Gansu, and other western provinces. Therefore, regional economic development remains significantly uneven. In China, the economic type of cities is relatively stable, and the probability of leapfrogging types is low; however, the level of cities with high resource dependence or a single economic structure easily downgrades. The level of economic development and the related socioeconomic factors of neighboring cities influence an obvious spatial spillover effect in the development of urban economies in China. The pattern of China's urban economic development is mainly affected by innovation capacity, financial support, capital investment, transportation infrastructure, and industrial structure.     

基于DMSP/OLS数据的中国城市化过程研究——反映区域城市化水平的灯光指数的构建

基于DMSP/OLS非辐射定标的夜间灯光平均强度数据构建了一个反映区域城市化水平的灯光指数 ,并分析了该灯光指数与城市化水平在省级尺度上的相关性。研究表明 ,灯光指数与反映城市化水平的复合指数存在较高的相关关系 ,可以用于我国城市化水平及其时空分异分析和城市化进程监测     

Developing spatially and thematically detailed backdated maps for land cover studies

ABSTRACT

Global or regional land cover change on a decadal time scale can be studied at a high level of detail using the availability of remote sensing data such as that provided by Landsat. However, there are three main technical challenges in this goal. First, the generation of land cover maps without reference data is problematic (backdating). Second, it is important to maintain high accuracies in land cover change map products, requiring a reasonably rich legend within each map. Third, a high level of automation is necessary to aid the management of large volumes of data. This paper describes a robust methodology for processing time series of satellite data over large spatial areas. The methodology includes a retrospective analysis used for the generation of training and test data for historical periods lacking reference information. This methodology was developed in the context of research on global change in the Iberian Peninsula. In this study we selected two scenes covering geographic regions that are representative of the Iberian Peninsula. For each scene, we present the results of two classifications (1985–1989 and 2000–2004 quinquennia), each with a legend of 13 categories. An overall accuracy of over 92% was obtained for all 4 maps.     

Analysis of regional labour market dynamics: In search of indications for self-organised criticality

Self-Organised Criticality (SOC) is a concept developed over the last decade from dynamic systems analysis that aims to investigate the transition trajectories of evolutionary systems. The main emphasis in SOC is on the analysis of the impact of slow exogenous forces in combination with strong localised interactions between single elements or components of the systems concerned, with particular regard to the attainment of a critical state at the macro-level. Against this background, SOC serves to identify the critical conditions from the internal dynamics of the systems that lead to major and significant transformations of their behaviour. The present paper aims to offer insights and reflections originating from the SOC concept, as well as to explore its potential contribution to understanding the evolution of regional economic patterns, in particular the functioning of regional labour markets. In our study, the evolutionary dynamics of employment at a district level in West Germany as well as in the combined West and East German setting will be investigated, in order to detect the possible existence of a power law distribution of growth rates, which may be seen as an indication for SOC at the macro-economic level. In this context, SOC may help to explain the presence of large socio-economic regional fluctuations in a country, and, in general, the dynamics inherent to regional development.     

基于GIS的区域经济统计分析系统的设计与实现

随着经济的发展,区域间经济相互作用和影响的关系越来越密切,忽略空间相互作用的传统区域经济分析手段和方法开始显现出一些明显的不足。而GIS在处理和分析区域地理数据上具有强大的空间分析能力,为此我们引入GIS工具对区域经济进行分析,相较于传统的分析手法有了很大改善。本文以江西省区域经济数据为例,设计了一个基于GIS的区域经济统计分析系统,并利用Arcob-jects二次开发控件结合VB可视化编程语言,实现了大部分区域经济统计分析功能,最后并将结果进行网络发布。研究结果表明将GIS引入到区域经济统计分析中,更能正确、有效的揭示区域经济发展在空间上的联系,为区域经济研究决策提供服务。     

Impact of FORMOSAT-3/COSMIC GPS radio occultation and dropwindsonde data on regional model predictions during the 2007 Mei-yu season

A study of the impact of FORMOSAT-3/COSMIC GPS radio occultation (RO) and dropwindsonde data on regional model simulations for a 11-day period during the 2007 Mei-yu season is presented. The Weather Research and Forecasting (WRF) model and its three-dimensional variation component, WRF-Var, are used for regional model predictions of heavy rainfall events in Taiwan. Without the use of GPS RO and dropwindsonde data, pressure and relative humidity are, in general, underestimated by the model; temperature predictions have a warm bias at the low level and a cold bias at the high level; and the east–west and north–south component winds have positive and negative biases, respectively. Incorporating GPS RO data tends to improve the prediction for longer integration. The assimilation of dropwindsonde data improves the forecast at the earlier time and at higher levels, and the improvement decreases over time. The reason the dropwindsonde data produce a positive impact earlier and the GPS RO data later is that there are few GPS RO observations in the fine domain. The large-scale simulation is first improved using the GPS RO observations, and the resulting changes can have a positive impact on the mesoscale at the later time. The dropwindsonde observations were taken inside the fine domain such that their impact can be detected early in the simulation. With both types of observation included, the prediction shows even greater improvement. At the earlier forecast time, there is nearly no impact from GPS and dropwindsonde data on rainfall forecasts. However, at the later integration time, the GPS data start to significantly improve the rainfall forecast. The dropwindsonde data also provide a positive impact on rainfall forecasts, but it is not as significant as that of the GPS data.     

Support Vector Machine for Spatial Variation

Large, multivariate geographic datasets have been used to characterize geographic space with the help of spatial data mining tools. In our study, we explore the sufficiency of the Support Vector Machine (SVM), a popular machine‐learning technique for unsupervised classification and clustering, to help recognize hidden patterns in a college admissions dataset. Our college admissions dataset holds over 10,000 students applying to an undisclosed university during one undisclosed year. Students are qualified almost exclusively by their standardized test scores and school records, and a known admissions decision is rendered based on these criteria. Given that the university has a number of political, social and geographic econometric factors in its admissions decisions, we use SVM to find implicit spatial patterns that may favor students from certain geographic regions. We first explore the characteristics of the applicants in the college admissions case study. Next, we explain the SVM technique and our unique ‘threshold line’ methodology for both discrete (regional) and continuous (k‐neighbors) space. We then analyze the results of the regional and k‐neighbor tests in order to respond to the methodological and geographic research questions.     

GIS技术在区域经济分析中的应用

讨论了GIS技术在区域经济分析中的应用,提出了一个基于GIS技术的区域经济分析系统。随后给出了实例分析——应用全国空间数据和统计数据(如GDP等)在ArcView GIS的系统环境下开发了一个区域经济差异特征分析模块,得出了全国各区域的差异情况,并对整个区域进行了经济分区,为区域经济规划的制定与决策提供了一种科学的依据。