Using multiple scale spatio-temporal patterns for validating spatially explicit agent-based models

Spatially explicit agent-based models (ABMs) have been widely utilized to simulate the dynamics of spatial processes that involve the interactions of individual agents. The assumptions embedded in the ABMs may be responsible for uncertainty in the model outcomes. To ensure the reliability of the outcomes in terms of their space-time patterns, model validation should be performed. In this article, we propose the use of multiple scale spatio-temporal patterns for validating spatially explicit ABMs. We evaluated several specifications of vector-borne disease transmission models by comparing space-time patterns of model outcomes to observations at multiple scales via the sum of root mean square error (RMSE) measurement. The results indicate that specifications of the spatial configurations of residential area and immunity status of individual humans are of importance to reproduce observed patterns of dengue outbreaks at multiple space-time scales. Our approach to using multiple scale spatio-temporal patterns can help not only to understand the dynamic associations between model specifications and model outcomes, but also to validate spatially explicit ABMs.     

Gen*: a generic toolkit to generate spatially explicit synthetic populations

Agent-based models tend to integrate more and more data that can deeply impact their outcomes. Among these data, the ones that deal with agent attributes and localization are particularly important, but are very difficult to collect. In order to tackle this issue, we propose a complete generic toolkit called Gen* dedicated to generating spatially explicit synthetic populations from global (census and GIS) data. This article focuses on the localization methods provided by Gen* that are based on regression, geometrical constraints and spatial distributions. The toolkit is applied for a case study concerning the generation of the population of Rouen (France) and shows the capabilities of Gen* regarding population spatialization.     

社会感知与地理空间智能的研究动态与展望——“社会感知与地理空间智能”专栏导读

地理大数据及其提供的社会感知手段为信息地理学、人文地理学带来了全新的研究范式,在“人—地”关系研究中发挥了重要价值。地理空间智能(GeoAI)作为地理空间科学与人工智能相结合的交叉研究方向,有力提升了对于地理现象和地球科学过程的动态感知、智能推理和知识发现的能力。该文系统综述了社会感知与地理空间智能研究的发展动态,在此基础上,对大数据时代社会感知研究面临的挑战和发展趋势进行了展望。     

Geosocial gauge: a system prototype for knowledge discovery from social media

The remarkable success of online social media sites marks a shift in the way people connect and share information. Much of this information now contains some form of geographical content because of the proliferation of location-aware devices, thus fostering the emergence of geosocial media – a new type of user-generated geospatial information. Through geosocial media we are able, for the first time, to observe human activities in scales and resolutions that were so far unavailable. Furthermore, the wide spectrum of social media data and service types provides a multitude of perspectives on real-world activities and happenings, thus opening new frontiers in geosocial knowledge discovery. However, gleaning knowledge from geosocial media is a challenging task, as they tend to be unstructured and thematically diverse. To address these challenges, this article presents a system prototype for harvesting, processing, modeling, and integrating heterogeneous social media feeds towards the generation of geosocial knowledge. Our article addresses primarily two key components of this system prototype: a novel data model for heterogeneous social media feeds and a corresponding general system architecture. We present these key components and demonstrate their implementation in our system prototype, GeoSocial Gauge.     

A spatially explicit methodology for a priori estimation of field survey effort in environmental observation networks

When establishing environmental monitoring programmes, it crucial to make reliable cost estimates, especially where a field survey is involved. This paper presents a methodology for creating a spatial measure of a field survey effort (SE). A set of relevant variables affecting a SE (e.g. areas with rough terrain, or distant from the main road network) was classified using fuzzy sets and then combined to produce spatially explicit effort indicators, which were integrated to a single measure using an analytic hierarchy process (AHP). To evaluate this approach and identify the limits for its application, three spatially nested case studies were used to test the spatial expression of SE and the scalable capacity of the method itself. The presented methodology could cope with variations in the scale and data resolution, retrieving a coherent estimate of SE across the different case studies. The presented methodology is therefore useful for (i) testing the network designs for sampling bias related to SE, (ii) comparing alternative sampling designs, (iii) assessing the sampling costs and (iv) supporting the human and logistical resource management.     

Areal interpolation of spatially extensive variables: A comparison of alternative techniques

Abstract

This paper presents a methodology which provides a practicable solution to a specific class of areal interpolation problems. The method allows for the transformation of the areal basis of land use data from a set of reporting units to a set of land capability classes within each of which land use mix is assumed to be homogeneous. Three alternative procedures, based on least squares criteria, are suggested for estimation of the land use mix in each land class. Although these are shown to produce similar and broadly plausible results in an empirical application, choice of procedure may be guided by consideration of the particular application and computational ease.     

Oil and gas development exposure and conservation scenarios for Greater sage-grouse: Combining spatially explicit modeling with GIS visualization provides critical information for management decisions

During the last quarter-century, global demand for energy has increased by more than 60%, and a similar increase is anticipated to occur by 2030 (Raymond, Deming, & Nichols, 2007). In the U.S., oil and gas development is projected to continue across western states within sage-grouse habitat. Greater sage-grouse, recently a candidate species for protection under the Endangered Species Act (ESA), have well documented negative responses to oil and gas disturbance. In this study, we create spatially-explicit oil and gas future development scenarios, baseline and high, and link them to sage-grouse population and habitat maps to quantify future exposure risk within Western Association of Fish and Wildlife Agencies (WAFWA) sage-grouse management zones (MZ) I and II. We then analyze recent land use decisions from the Bureau of Land Management (BLM) along with enacted policy from the State of Wyoming to estimate how these management actions might minimize the exposure risk of sage-grouse to oil and gas development into the future. Our results show that BLM and Wyoming conservation plans could reduce the exposure of sage-grouse to oil and gas development from 15-27% to 11–17% (31–37% reduction) in MZ I and from 15-27% to 5–9% (64–68% reduction) in MZ II. Our estimates of exposure to future oil and gas development, and conservation measures designed to ameliorate those threats, represent the upper and lower extents of potential impacts within scenarios. Our work demonstrates how spatial modeling and GIS visualization can be used by managers to assess likely outcomes of conservation decisions.     

Predicting 21st century global agricultural land use with a spatially and temporally explicit regression-based model

The extensive alteration of the earth's land cover during the anthropocene had widespread, and in some cases unknown, effects on terrestrial and atmospheric conditions and processes. Predicting future changes to the earth system therefore mandate a future-predicting framework of land use dynamics. However while future-predicting earth surface and atmospheric models tend to explicitly incorporate projected climatic conditions they all but ignore or overly simplify land use dynamics. As most surface and atmosphere dynamics models use gridded input datasets, and land use is a highly spatially-dynamic phenomena, a need clearly arise for spatially explicit representation of future land use dynamics. While a number of such datasets exists at regional and country scales, no fully gridded future-predicting global land use model and database has been reported to date. Here we present the Global Land Use Dynamics Model (GLUDM), a gridded and temporally explicit agricultural land use predictor. GLUDM calculates the relative area of a land use category (e.g. cropland) in each grid-cell by generating unique regression coefficients in each grid-cell based on local historic trends and global population dynamics. Spatial expansions or abandonment of agricultural land is simulated by propagating excesses or deficiencies in agricultural areas between neighboring grid-cells. This spatial connectivity is restricted by topographic, latitudinal and urban characteristics. A validation analysis shows that GLUDM corresponds well to observed land use distribution. GLUDM-predicted global cropland area dynamics between 2005 and 2100 are described herein. Globally, 18% increase in cropland area is predicted between 2005 and 2050 which corresponds very well to previous estimations. Following 2050, a general decrease in cropland area is predicted. The results reveal new insights about global cropland dynamics, demonstrating, for example, that changes in its spatial distribution will be highly heterogeneous, at both micro and macro scales, in some locations worldwide.     

Application of a spatially explicit backcasting model: A case study of sustainable development in Salzburg,Austria

The high cost of land for housing within urban centres and the common desire to live within extensive residential areas in the green countryside have, in some cases, led to increasing residential development in the urban hinterland, often resulting in dispersed and sprawling development. In order to counteract such development this article seeks to provide a methodology for implementing strategies which aim is to achieve sustainable development in spatial planning. The proposed methodology, which is known as “backcasting”, aims to improve our ability to avoid undesirable future developments and to encourage those developments that are desirable. Backcasting has previously mostly been used within theoretical processes or frameworks. The backcasting exercise presented in this paper used a Python-based model to create often visionary future scenarios based on interviews with relevant experts, and then used these scenarios as input for a backwards running model. This model simulates a development that runs backwards in time, converging towards the present situation. The backcasting model presented herein has been applied to a case study in Salzburg, Austria.     

遥感影像分类与地学知识发现的集成研究

遥感与地学之间存在着数据与知识上巨大的互补性。本文通过地面类型数据将遥感影像分类与地学知识发现结合起来:用遥感数据驱动发现地学知识,用地学知识解释、确认、检验遥感分类结果,并使用统计值和分布谱来定量化表达地学知识,形成一体化的遥感地学分类系统。     

The study of artificial intelligence for predicting land use changes in an arid ecosystem

Journal of Geographical Sciences - During the 21st century, artificial intelligence methods have been broadly applied in geosciences to simulate complex dynamic ecosystems, but the use of...     

VGTrust: measuring trust for volunteered geographic information

Volunteered Geographic Information (VGI) has emerged as a large, up-to-date, and easily accessible data source. VGI can allow authoritative mapping agencies to undertake continuous improvement of their own data, adding a currency dimension previously unattainable due to high associated costs. VGI also benefits scientific and social research by facilitating quick and low-cost research data capture by the public. VGI, however, through its diversity of authorship, presents a quality assurance risk to the use of this data. This research presents a formulaic model that addresses VGI quality issues, by quantifying trust in VGI. Our ‘VGTrust’ model assesses information about a data author, and the spatial and temporal trust associated with the data they create, to produce an overall VGTrust rating metric. This metric is both easy to understand and interpret. A facilitated case study, ‘Building Our Footprints’ is presented which tests the feasibility of VGTrust model in a real-world data capture exercise run by Land Information New Zealand, New Zealand’s mapping organisation. By overcoming the trust issues in VGI, this research will allow the integration of VGI and authoritative data and potentially expand the application of VGI, thereby leveraging the power of the crowd for productive and innovative re-use.     

Land use legacies of the Ohio River Basin: Using a spatially explicit land use change model to assess past and future impacts on aquatic resources

Land uses and their legacies are a major driver of human impacts on the environment. Decision makers have recognized that the legacies of land-use activities continue to influence ecosystems, particularly aquatic ones, for decades or centuries. The main objectives of this paper are to develop land use legacy maps to (1) assess historical and future (predicted) shifts in dominant land use classes (urban, agriculture, forest) in the Ohio River Basin (ORB), and (2) determine the past and future location of catchments in the ORB exceeding critical land use thresholds (10% and 38% urban and agricultural use of catchment respectively) for water quality and other aquatic resources. Our land use legacy simulations show that approximately 80% of the ORB has remained as agriculture (∼37%), forest (∼34%), urban (∼7%) and other classes (∼2%) from 1930 to 1990. Within the remainder of landscape, agriculture to forest (∼16%) and agriculture to urban (∼1.5%) transitions were the most common land use changes between 1930 and 1990. Our forecast model shows that approximately 94% of the ORB will remain as forest (∼47.46%), agriculture (∼35.77%), urban (∼8.84%) and other classes (∼2.07%) between 2000 and 2050. 1.44% and 1.37% of the ORB is predicted to transition from forest to urban and agriculture to urban between 2000 and 2050, respectively. Our results also demonstrate that 13% and 74% of the catchments in the ORB already exceeded critical urban and agricultural land use thresholds in 1930, respectively. We predict that 37% of catchments in the ORB will have exceeded critical urban land use thresholds by 2050, whereas the proportion of catchments to exceed critical agricultural use will decrease to 45%.     

Mining urban land-use patterns from volunteered geographic information by means of genetic algorithms and artificial neural networks

In the context of OpenStreetMap (OSM), spatial data quality, in particular completeness, is an essential aspect of its fitness for use in specific applications, such as planning tasks. To mitigate the effect of completeness errors in OSM, this study proposes a methodological framework for predicting by means of OSM urban areas in Europe that are currently not mapped or only partially mapped. For this purpose, a machine learning approach consisting of artificial neural networks and genetic algorithms is applied. Under the premise of existing OSM data, the model estimates missing urban areas with an overall squared correlation coefficient (R ²) of 0.589. Interregional comparisons of European regions confirm spatial heterogeneity in the model performance, whereas the R ² ranges from 0.129 up to 0.789. These results show that the delineation of urban areas by means of the presented methodology depends strongly on location.     

Modelling spatially and temporally distributed phenomena: new methods and tools for GRASS GIS

Abstract

The concept of GRASS (Geographic Resources Analysis Support System) as an open system has created a favourable environment for integration of process based modelling and GIS. To support this integration a new generation of tools is being developed in the following areas: (a) interpolation from multidimensional scattered point data, (b) analysis of surfaces and hypersurfaces, (c) modelling of spatial processes and, (d) 3D dynamic visualization. Examples of two applications are given-spatial and temporal modelling of erosion and deposition, and multivariate interpolation and visualization of nitrogen concentrations in the Chesapeake Bay.