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1.
Socio‐demographic data are typically collected at various levels of aggregation, leading to the modifiable areal unit problem. Spatial non‐stationarity of statistical associations between variables further influences the demographic analyses. This study investigates the implications of these two phenomena within the context of migration‐environment associations. Global and local statistical models are fit across increasing levels of aggregation using household level survey data from rural South Africa. We raise the issue of operational scale sensitivity, which describes how the explanatory power of certain variables depends on the aggregation level. We find that as units of analysis (households) are aggregated, some variables become non‐significant in the global models, while others are less sensitive to aggregation. Local model results show that aggregation reduces spatial variation in migration‐related local associations but also affects variables differently. Spatial non‐stationarity appears to be the driving force behind this phenomenon as the results from the global model mask this relationship. Operational scale sensitivity appears related to the underlying spatial autocorrelation of the non‐aggregated variables but also to the way a variable is constructed. Understanding operational scale sensitivity can help to refine the process of selecting variables related to the scale of analysis and better understand the effects of spatial non‐stationarity on statistical relationships.  相似文献   

2.
Spatial accessibility is an enduring topic of spatial analysis that is intimately tied to issues of spatial representation and scale. A variety of methods to measure accessibility have been developed with most research focusing on metropolitan‐sized spatial extents using census‐defined aggregation units and relying on vector point representation to calculate Euclidean or network distances as key ingredients in measure formulations. Less research considers broader scales where both origin and destination points are treated as polygons. This research develops alternative gravity‐based measures of polygon‐to‐polygon accessibility for a case study of county‐level accessibility to national forests in the western US. Different methods of county and forest representation are implemented using census block centroids and a lattice approach for disaggregation and re‐aggregation. Other characteristics that are analyzed include origin‐destination linkage definitions, population weighting, and distance thresholds. Correlation analysis is used to assess relationships of alternative measures with a simple percentage measure and with each other. Low correlations would suggest that measures capture different aspects of accessibility that are related to their qualitative characteristics. Results show the alternative measures to be dissimilar from the percentage measure; however, high correlations among alternative measures suggest that there is little to differentiate certain disaggregated measures in spite of their richer qualitative interpretation.  相似文献   

3.
Recent technical advances in remote sensing data capture and spatial resolution lead to a widening gap between increasing data availability on the one hand and insufficient methodology for semi-automated image data processing and interpretation on the other hand. At the interface of GIS and remote sensing, object-based image analysis methodologies are one possible approach to close this gap. With this, methods from either side are integrated to use both the capabilities of information extraction from image data and the power to perform spatial analysis on derived polygon data. However, dealing with image objects from various sources and in different scales implies combining data with inconsistent boundaries. A landscape interpretation support tool (LIST) is introduced which seeks to investigate and quantify spatial relationships among image objects stemming from different sources by using the concept of spatial coincidence. Moreover, considering different categories of object fate, LIST enables a change categorization for each polygon of a time series of classifications. The application of LIST is illustrated by two case-studies, using Landsat TM and ETM as well as CIR aerial photographs: the first showing how the tool is used to perform object quantification and change analysis; the latter demonstrating how superior aggregation capabilities of the human brain can be combined with the fine spatial segmentation and classification. Possible fields of application are identified and limitations of the approach are discussed.  相似文献   

4.
Spatial object and spatial relationship are two basic concepts of GIS.Spatial object is the digital representation of geographical entity or phenomenon,which forms the basis for data management and analysis;spatial relationship is theconnexion between spatial objects when geometric properties are considered.Thecommonly used classification of spatial objects as points,lines and areas is mathe-matically strict,and suitable for data management,but a bit too generalized forrepresenting real entities and extracting spatial relationships.A good classificationmodel should not only be good for representing real entities,but also good for re-vealing spatial relationships,therefore good for formalizing spatial analyses.  相似文献   

5.
采用探索性空间数据分析的方法,利用2005—2014年的青海省县域人均GDP数据,从时空的角度对青海省县域经济差异进行分析。分析结果表明:(1)青海省县域经济发展水平差异较大,且有进一步加大的趋势,青海西北部经济发展水平较高。(2)青海省县域经济具有显著的空间正相关关系,且这种关系在加强,县域经济联系愈加紧密。(3)青海省县域经济发展空间集聚作用明显,逐渐形成了"HH"和"LL"的空间分布格局。"HH"的聚集范围为海西蒙古族藏族自治州、德令哈市等的海西区域,"LL"的聚集范围为玉树县、达日县等的海南区域。然后,从经济发展基础、经济发展区位、空间邻近效应分析影响青海省县域经济差异的原因,并结合空间特征提出相应建议。  相似文献   

6.
研究高速公路交通事故黑点路段的时空分布规律和关联因素,一直是交通领域的关注重点。本文针对事故统计的交通事故黑点路段鉴别方法存在地理学中的可塑面积单元(MAUP)问题,提出一种基于时空密度聚类的高速公路交通事故黑点路段鉴别方法。该方法改进了传统的DBSCAN空间聚类算法,引入一种顾及时间周期性和事故严重程度的事故时空邻近计算方法,通过密度连接规则自适应鉴别各种时空尺度的交通事故黑点路段。以2012—2016年湖南省的高速公路交通事故为例进行试验,结果表明,本文方法可有效克服不同划分单元的可塑面积单元问题,自适应鉴别不同长度的黑点路段,同时可进一步挖掘黑点路段上交通事故时空聚集模式。  相似文献   

7.
Two geographically related questions with regard to hurricane-induced storm-surge impacts were investigated: (1) What observational scale of analysis is appropriate? (2) Is the effect of observational scale on model results predictable? These two research questions were investigated in the context of storm surge-induced impacts to single-family residential structures in Florida. The study was conducted for 21 coastal counties in Florida at five spatial scales of analysis: parcel, block, block group, tract, and county. The research findings reveal a monotonically decreasing relationship between predicted standardized residential loss (the ratio of predicted loss at scale X and the predicted loss at parcel scale) and the observational scale of analysis. This monotonic relationship was consistent for most Florida counties, primarily due to the notable spatial distribution of housing units and proximity to the coastline.  相似文献   

8.
ABSTRACT

Massive social media data produced from microblog platforms provide a new data source for studying human dynamics at an unprecedented scale. Meanwhile, population bias in geotagged Twitter users is widely recognized. Understanding the demographic and socioeconomic biases of Twitter users is critical for making reliable inferences on the attitudes and behaviors of the population. However, the existing global models cannot capture the regional variations of the demographic and socioeconomic biases. To bridge the gap, we modeled the relationships between different demographic/socioeconomic factors and geotagged Twitter users for the whole contiguous United States, aiming to understand how the demographic and socioeconomic factors relate to the number of Twitter users at county level. To effectively identify the local Twitter users for each county of the United States, we integrate three commonly used methods and develop a query approach in a high-performance computing environment. The results demonstrate that we can not only identify how the demographic and socioeconomic factors relate to the number of Twitter users, but can also measure and map how the influence of these factors vary across counties.  相似文献   

9.
基于SMCS的多源空间数据集成应用   总被引：1，自引：0，他引：1  
为解决多源空间数据特别是不同参考基准数据实时集成困难的问题,通过分析空间元数据间关系,定义并利用元数据知识来实现空间元数据的动态聚合和目录服务SMCS的构建。实验结果表明顾及空间关系的SMCS提高了对空间信息的动态获取、处理和发布的能力和效率,为网格环境下多源数据的高效集成应用服务提供技术支持。  相似文献   

10.
Using geographic information systems to link administrative databases with demographic, social, and environmental data allows researchers to use spatial approaches to explore relationships between exposures and health. Traditionally, spatial analysis in public health has focused on the county, ZIP code, or tract level because of limitations to geocoding at highly resolved scales. Using 2005 birth and death data from North Carolina, we examine our ability to geocode population‐level datasets at three spatial resolutions – zip code, street, and parcel. We achieve high geocoding rates at all three resolutions, with statewide street geocoding rates of 88.0% for births and 93.2% for deaths. We observe differences in geocoding rates across demographics and health outcomes, with lower geocoding rates in disadvantaged populations and the most dramatic differences occurring across the urban‐rural spectrum. Our results suggest that highly resolved spatial data architectures for population‐level datasets are viable through geocoding individual street addresses. We recommend routinely geocoding administrative datasets to the highest spatial resolution feasible, allowing public health researchers to choose the spatial resolution used in analysis based on an understanding of the spatial dimensions of the health outcomes and exposures being investigated. Such research, however, must acknowledge how disparate geocoding success across subpopulations may affect findings.  相似文献   

11.
To overcome the weakness of modelling the relationships between map objects that represent the same geographic entities or phenomena at different times and scales, an agent-based approach is presented to modelling of the spatial evolution of map objects for better understanding of the evolutional process of different maps. Map agents are used to establish and manage the many-to-many corresponding relationships between map objects, and they render map objects active rather than traditionally passive. The evolutions of map objects are modelled by map agents using association and generation relationships to model the connectivity between map objects. This effort reduces the workload of multi-scale map updating by avoiding update of the entire map; instead, it simply allows map agents to detect, perceive and choose suitable behaviours on which to operate and update a spatially evolving object. Experiments of specific examples are presented to demonstrate the feasibility and the effectiveness of our approach.  相似文献   

12.
ABSTRACT

There is a critical need to develop a means for fast, task-driven discovery of geospatial data found in geoportals. Existing geoportals, however, only provide metadata-based means for discovery, with little support for task-driven discovery, especially when considering spatial–temporal awareness. To address this gap, this paper presents a Case-Based Reasoning-supported Geospatial Data Discovery (CBR-GDD) method and implementation that accesses geospatial data by tasks. The advantages of the CBR-GDD approach is that it builds an analogue reasoning process that provides an internal mechanism bridging tasks and geospatial data with spatial–temporal awareness, thus providing solutions based on past tasks. The CBR-GDD approach includes a set of algorithms that were successfully implemented via three components as an extension of geoportals: ontology-enhanced knowledge base, similarity assessment model, and case retrieval nets. A set of experiments and case studies validate the CBR-GDD approach and application, and demonstrate its efficiency.  相似文献   

13.
Abstract

Landsat Thematic Mapper (TM) data have been used to monitor land cover types and to estimate biophysical parameters. However, studies examining the spatial relationships between land cover change and biophysical parameters are generally lacking. With the integration of remote sensing and Geographic Information Systems (GIS), these relationships can be better explored. The research reported in this paper applies this integrated approach for detecting urban growth and assessing its impact on vegetative greenness in the Zhujiang Delta, China. Multi‐temporal Landsat TM data were utilized to map urban growth and to extract and identify changes in vegetative greenness. GIS analyses were conducted to examine the changing spatial patterns of urban growth and greenness change. Statistical analyses were then used to examine the impact of urban growth on vegetative greenness. The results revealed that there was a notably uneven urban growth pattern in the delta, and urban development had reduced the scaled Normalized Difference Vegetation Index (NDVI) value by 30% in the urbanized area.  相似文献   

14.
This study evaluates how watershed discretization affects estimation of hydrologic parameters using GIS data. Two aggregation methods were evaluated using three GIS data sets for a large watershed in Kansas, which is discretized into five different levels. The two aggregation methods are weighted-average and dominant-value. The three GIS data sets, soils, land use, and temperature, constitute three commonly used hydrologic parameters with distinct spatial patterns. The study evaluated the aggregation effects measured in terms of statistical distribution, spatial distribution, information level, and spatial dependence of the aggregated data. Results indicate that: (1) statistically, the mean and modal values of the source data are well preserved through aggregation but with a reduced standard deviation; (2) changes in spatial patterns are less predictable than those of the statistical distribution, and the changes depend on the geometric similarity and spatial overlap between the source and target polygons; (3) the information level in general decreases with aggregation for the dominant method, and it increases for the average method although the original values are altered; and (4) spatial dependence generally increases with aggregation.  相似文献   

15.
Effects of scale in spatial interaction models   总被引：1，自引：0，他引：1  
We study the effects of aggregation on four different cases of nonlinear spatial gravity models. We present some theoretical results on the relationship between the mean flows at an aggregated level and the mean flow at the disaggregated level. We then focus on the case of perfect aggregation (scale problem) showing some results based on the theoretical expressions previously derived and on some artificial data. The main aim is to test the effects on the aggregated flows of the spatial dependence observed in the origin and in the destination variables. We show that positive spatial dependence in the origin and destination variables moderate the increase of the mean flows connatural with aggregation while negative spatial dependence exacerbates it.  相似文献   

16.
ABSTRACT

The mean height-for-age Z-score (HAZ) of children under five is an important indicator of the health status of a population. HAZ values are frequently aggregated and reported at the national level, potentially obscuring important within-country variation. We evaluated aggregation and interpolation methods to provide sub-national estimates over space and time, using survey data from Nigeria in 1990, 2003, 2008, and 2013. We aggregated HAZ values by region and by state, and interpolated the values spatially and spatio-temporally using thin plate splines. The results were evaluated with cross-validation using the root mean squared error (RMSE) as a measure of goodness of fit. Mean HAZ values increased from 1990 to 2013, but values rose more sharply in southern Nigeria than in the North. All methods performed better than assuming a constant national average. The state-level aggregation, and the spatial and spatio-temporal interpolations had similar RMSE values, but the interpolation methods showed more detailed spatial variation. Spatio-temporal interpolation produced good results in all conditions, including in years with sparse sampling and when extrapolating to years for which there were no observations.  相似文献   

17.
This paper describes techniques to compute and map dasymetric population densities and to areally interpolate census data using dasymetrically derived population weights. These techniques are demonstrated with 1980-2000 census data from the 13-county Atlanta metropolitan area. Land-use/land-cover data derived from remotely sensed satellite imagery were used to determine the areal extent of populated areas, which in turn served as the denominator for dasymetric population density computations at the census tract level. The dasymetric method accounts for the spatial distribution of population within administrative areas, yielding more precise population density estimates than the choroplethic method, while graphically representing the geographic distribution of populations. In order to areally interpolate census data from one set of census tract boundaries to another, the percentages of populated areas affected by boundary changes in each affected tract were used as adjustment weights for census data at the census tract level, where census tract boundary shifts made temporal data comparisons difficult. This method of areal interpolation made it possible to represent three years of census data (1980, 1990, and 2000) in one set of common census tracts (1990). Accuracy assessment of the dasymetrically derived adjustment weights indicated a satisfactory level of accuracy. Dasymetrically derived areal interpolation weights can be applied to any type of geographic boundary re-aggregation, such as from census tracts to zip code tabulation areas, from census tracts to local school districts, from zip code areas to telephone exchange prefix areas, and for electoral redistricting.  相似文献   

18.
Recent advances in time geography offer new perspectives for studying animal movements and interactions in an environmental context. In particular, the ability to estimate an animal's spatial location probabilistically at temporal sampling intervals between known fix locations allows researchers to quantify how individuals interact with one another and their environment on finer temporal and spatial scales than previously explored. This article extends methods from time geography, specifically probabilistic space–time prisms, to quantify and summarize animal–road interactions toward understanding related diurnal movement behaviors, including road avoidance. The approach is demonstrated using tracking data for fishers (Martes pennanti) in New York State, where the total probability of interaction with roadways is calculated for individuals over the duration tracked. Additionally, a summarization method visualizing daily interaction probabilities at 60 s intervals is developed to assist in the examination of temporal patterns associated with fishers’ movement behavior with respect to roadways. The results identify spatial and temporal patterns of fisher–roadway interaction by time of day. Overall, the methodologies discussed offer an intuitive means to assess moving object location probabilities in the context of environmental factors. Implications for movement ecology and related conservation planning efforts are also discussed.  相似文献   

19.
为了健全现行国土空间规划体系中市县尺度主体功能区的划定方法。该文以主体功能区基本理论为指导,综合地理国情信息中的地表现状数据与DEM数据,结合专题调查与经济社会统计数据等空间信息,利用GIS空间分析、多元统计分析、基于规则的分类模型等技术,研究细化上位主体功能区进行市县主体功能区划定的方法、指标与分类体系、与上位主体功能区的衔接机制。提出了一种基于局部功能单元的市县主体功能分区方法和流程。并以榆林市为实证研究对象,进行实证研究,划定了榆林市42920 km2的市县级主体功能分区。探索将主体功能分区的空间尺度延伸到具体的地理单元,发挥主体功能区在国土空间规划体系中基础性作用,为主体功能区战略格局在市县层面落地提供科学依据。  相似文献   

20.
Abstract

Geographically weighted regression (GWR) is a local spatial statistical technique for exploring spatial nonstationarity. Previous approaches to mapping the results of GWR have primarily employed an equal step classification and sequential no-hue colour scheme for choropleth mapping of parameter estimates. This cartographic approach may hinder the exploration of spatial nonstationarity by inadequately illustrating the spatial distribution of the sign, magnitude, and significance of the influence of each explanatory variable on the dependent variable. Approaches for improving mapping of the results of GWR are illustrated using a case study analysis of population density–median home value relationships in Philadelphia, Pennsylvania, USA. These approaches employ data classification schemes informed by the (nonspatial) data distribution, diverging colour schemes, and bivariate choropleth mapping.  相似文献   

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