Exploring the relationship between density and completeness of urban building data in OpenStreetMap for quality estimation

OpenStreetMap (OSM) is a free spatial data source based on crowd sourced data. Although the OSM data have a range of applications, such as generating 3D models, and routing and navigation, quality issues are still significant concerns when using the data. Several studies have undertaken quality assessments by comparing OSM data with reference data. However, reference data are not always available due to high costs or licensing restrictions, and very few studies have quantitatively estimated the quality of OSM data under conditions where the corresponding reference data are not available. This study proposed the use of a building density (or building coverage ratio) indicator as a proxy, and designed a series of experiments involving different study areas to quantitatively explore the relationship between building density and building completeness for OSM data in urban areas. The residuals (estimated building completeness and reference building completeness) were also analyzed. Two main results were found from the experiments. (1) There was an approximate linear relationship between building density and building completeness in the OSM data. More precisely, the building completeness of OSM data was approximately 3.4–4 times the building density of OSM data. (2) Approximately 70–80% of the absolute residuals were smaller than 10%, and 80–90% of them were smaller than 20%. This shows that, in most cases, estimated building completeness was close to the corresponding reference building completeness. Therefore, we concluded that the building density indicator is a potential proxy for the quantitative completeness estimation of OSM building data in urban areas. The limitations of using this indicator were also addressed.     

Predicting human mobility with activity changes

Human mobility patterns can provide valuable information in understanding the impact of human behavioral regularities in urban systems, usually with a specific focus on traffic prediction, public health or urban planning. While existing studies on human movement have placed huge emphasis on spatial location to predict where people go next, the time dimension component is usually being treated with oversimplification or even being neglected. Time dimension is crucial to understanding and detecting human activity changes, which play a negative role in prediction and thus may affect the predictive accuracy. This study aims to predict human movement from a spatio-temporal perspective by taking into account the impact of activity changes. We analyze and define changes of human activity and propose an algorithm to detect such changes, based on which a Markov chain model is used to predict human movement. The Microsoft GeoLife dataset is used to test our methodology, and the data of two selected users is used to evaluate the performance of the prediction. We compare the predictive accuracy (R²) derived from the data with and without implementing the activity change detection. The results show that the R² is improved from 0.295 to 0.762 for the user with obvious activity changes and from 0.965 to 0.971 for the user without obvious activity changes. The method proposed by this study improves the accuracy in analyzing and predicting human movement and lays the foundation for related urban studies.     

Spatio-temporal modeling of PM2.5 concentrations with missing data problem: a case study in Beijing,China

ABSTRACT

One of the major challenges in conducting epidemiological studies of air pollution and health is the difficulty of estimating the degree of exposure accurately. Fine particulate matter (PM_2.5) concentrations vary in space and time, which are difficult to estimate in rural, suburban and smaller urban areas due to the sparsity of the ground monitoring network. Satellite retrieved aerosol optical depth (AOD) has been increasingly used as a proxy of ground PM_2.5 observations, although it suffers from non-trivial missing data problems. To address these issues, we developed a multi-stage statistical model in which daily PM_2.5 concentrations can be obtained with complete spatial coverage. The model consists of three stages – an inverse probability weighting scheme to correct non-random missing patterns of AOD values, a spatio-temporal linear mixed effect model to account for the spatially and temporally varying PM_2.5-AOD relationships, and a gap-filling model based on the integrated nested Laplace approximation-stochastic partial differential equations (INLA-SPDE). Good model performance was achieved from out-of-sample validation as shown in R² of 0.93 and root mean square error of 9.64 μg/m³. The results indicated that the multi-stage PM_2.5 prediction model proposed in the present study yielded highly accurate predictions, while gaining computational efficiency from the INLA-SPDE.     

CO2 seasonal variation and global change: Test global warming from another point of view

CO₂ and temperature records at Mauna Loa, Hawaii, and other observation stations show that the correlation between CO₂ and temperature is not significant. These stations are located away from big cities, and in various latitudes and hemispheres. But the correlation is significant in global mean data. Over the last five decades, CO₂ has grown at an accelerating rate with no corresponding rise in temperature in the stations. This discrepancy indicates that CO₂ probably is not the driving force of temperature change globally but only locally (mainly in big cities). We suggest that the Earth''s atmospheric concentration of CO₂ is too low to drive global temperature change. Our empirical perception of the global warming record is due to the urban heat island effect:temperature rises in areas with rising population density and rising industrial activity. This effect mainly occurs in the areas with high population and intense human activities, and is not representative of global warming. Regions far from cities, such as the Mauna Loa highland, show no evident warming trend. The global monthly mean temperature calculated by record data, widely used by academic researchers, shows R²=0.765, a high degree of correlation with CO₂. However, the R² shows much less significance (mean R²=0.024) if calculated by each record for 188 selected stations over the world. This test suggests that the inflated high correlation between CO₂ and temperature (mean R²=0.765-0.024=0.741) used in reports from the Intergovernmental Panel on Climate Change (IPCC) was very likely produced during data correction and processing. This untrue global monthly mean temperature has created a picture:human emission drives global warming.     

Estimating the count of completeness errors in geographic data sets by means of a generalized Waring regression model

In this article, we propose a statistical model for estimating the probable number of completeness errors (omissions plus commissions) in a cell (a map tile or cluster) of a data set to guide updating or improvement efforts. The number of completeness errors is a count data variable related to some exogenous covariates that may also be known for each cell (e.g. count of features, rural or urban typology, etc.) and to other unknown variation sources. We propose and adjust a generalized Waring regression model for counting these errors in cells of 1 × 1 km² on the Topographic Map of Andalusia (Spain). This model is compared with the Poisson regression model and the negative binomial regression model and performs better. The empirical relationship established by the model indicates that the number of completeness errors is related to the following exogenous covariates: the number of cartographic features of the data set, the fact that the cell covers a littoral or urban zone and the spatial division of the contracted suppliers. For cells having less than 5 errors, most of the variability corresponds to unknown external factors (liability), but when the number of errors rises, the greater part of the variability is due to unknown internal characteristics of each cell (proneness). With these estimations, the producer can derivate statistical summaries and spatial representations and develop better planning of production activities such as actualization.     

Soil erosion risk scenarios in the Mediterranean environment using RUSLE and GIS: An application model for Calabria (southern Italy)

Soil erosion by water (WSE) has become a relevant issue at the Mediterranean level. In particular, natural conditions and human impact have made the Calabria (southern Italy) particularly prone to intense WSE. The purpose of this investigation is to identify areas highly affected by WSE in Calabria by comparing the scenarios obtained by assuming control and preventive measures and actions, as well as actual conditions generated by forest fires, also in the presence of conditions of maximum rainfall erosion.Geographic Information System techniques have been adopted to treat data of reasonable spatial resolution obtained at a regional scale for application to the RUSLE model. This work is based on the comparison of such data with a basic scenario that has been defined by the present situation (present scenario). In this scenario: (i) R has been assessed by means of an experimental relation adjusted to Calabria on the basis of 5-min observations; (ii) K has been drawn from the soil map of Calabria including 160 soilscapes; (iii) LS has been estimated according to the RUSLE2 model by using (among other subfactors) a 40-m square cell DTM; (iv) C has been derived by processing the data inferred from the project Corine Land Cover, whose legend includes 35 different land uses on three levels; and (v) P has been hypothesized as equal to 1.For the remaining three hypothesized scenarios, the RUSLE factors have been adjusted according to experimental data and to data in the literature. In particular, forest areas subject to fire have been randomly generated as far as fire location, extension, structure, and intensity are concerned.The values obtained by the application of the RUSLE model have emphasized that land management by means of measures and actions for reducing WSE causes a notable reduction of the erosive rate decreasing from ~30 to 12.3 Mg ha^− 1 y^− 1. On the other hand, variations induced by hypothetical wildfires in forests on 10% of the regional territory bring WSE over the whole region to values varying from 30 to 116 Mg ha^− 1 y^− 1.This study can be offered to territorial planning authorities as an evaluation instrument as it highlights the merits and limitations of some territorial management actions. In fact, in Calabria no observations exist concerning the implications of these actions.     

An evaluation of crowdsourced information for assessing the visitation and perceived importance of protected areas

Parks and protected areas provide a wide range of benefits, but methods to evaluate their importance to society are often ad hoc and limited. In this study, the quality of crowdsourced information from Public Participation GIS (PPGIS) and Volunteered Geographic Information (VGI) sources (Flickr, OpenStreetMap (OSM), and Wikipedia) was compared with visitor counts that are presumed to reflect social importance. Using the state of Victoria, Australia as a case study, secondary crowdsourced VGI data, primary crowdsourced (PPGIS data) and visitor statistics were examined for their correspondence and differences, and to identify spatial patterns in park popularity. Data completeness—the percent of protected areas with data—varied between sources, being highest for OSM (90%), followed by Flickr (41%), PPGIS (24%), visitation counts (5%), and Wikipedia articles (4%). Statistically significant correlations were found between all five measures of popularity for protected areas. Using stepwise multiple linear regression, the explained variability in visitor numbers was greater than 70%, with PPGIS, Flickr and OSM having the largest standardized coefficients. The social importance of protected areas varied as a function of accessibility and the types of values (direct or indirect use) expressed for the areas. Crowdsourced data may provide an alternative to visitor counts for assessing protected area social importance and spatial variability of visitation. However, crowdsourced data appears to be an unreliable proxy for the full range of values and importance of protected areas, especially for non-use values such as biological conservation.     

Quality assessment of building footprint data using a deep autoencoder network

Volunteered geographic information (VGI), OpenStreetMap (OSM), has been used in many applications, especially when official spatial data are unavailable or outdated. However, the quality of VGI remains a valid concern. In this paper, we use the matched results between OSM building footprints and official data as the samples for training an autoencoder network, which encodes and reconstructs the sample populations according to unknown complex multivariate probability distributions. Then, the OSM data are assessed based on the theory that small probability samples contribute little to the autoencoder network and that they can be recognized by the higher reconstructed errors during training. In the method described here, the selected measures, including data completeness, positional accuracy, shape accuracy, semantic accuracy and orientation consistency between OSM and official data, are used as the inputs for a deep autoencoder network. Finally, building footprint data from Toronto, Canada, are evaluated, and experiments show that the proposed method can assess the OSM data comprehensively, objectively and accurately.     

Quality assessment for building footprints data on OpenStreetMap

In the past two years, several applications of generating three-dimensional (3D) buildings from OpenStreetMap (OSM) have been made available, for instance, OSM-3D, OSM2World, OSM Building, etc. In these projects, 3D buildings are reconstructed using the buildings’ footprints and information about their attributes, which are documented as tags in OSM. Therefore, the quality of 3D buildings relies strongly on the quality of the building footprints data in OSM. This article is dedicated to a quality assessment of building footprints data in OSM for the German city of Munich, which is one of the most developed cities in OSM. The data are evaluated in terms of completeness, semantic accuracy, position accuracy, and shape accuracy by using building footprints in ATKIS (German Authority Topographic–Cartographic Information System) as reference data. The process contains three steps: finding correspondence between OSM and ATKIS data, calculating parameters of the four quality criteria, and statistical analysis. The results show that OSM footprint data in Munich have a high completeness and semantic accuracy. There is an offset of about four meters on average in terms of position accuracy. With respect to shape, OSM building footprints have a high similarity to those in ATKIS data. However, some architectural details are missing; hence, the OSM footprints can be regarded as a simplified version of those in ATKIS data.     

Incorporation of multi-scale spatial autocorrelation in soil moisture–landscape modeling

Effects of spatial autocorrelation (SAC), or spatial structure, have often been neglected in the conventional models of pedogeomorphological processes. Based on soil, vegetation, and topographic data collected in a coastal dunefield in western Korea, this research developed three soil moisture–landscape models, each incorporating SAC at fine, broad, and multiple scales, respectively, into a non-spatial ordinary least squares (OLS) model. All of these spatially explicit models showed better performance than the OLS model, as consistently indicated by R², Akaike’s information criterion, and Moran’s I. In particular, the best model was proved to be the one using spatial eigenvector mapping, a technique that accounts for spatial structure at multiple scales simultaneously. After including SAC, predictor variables with greater inherent spatial structure underwent more reduction in their predictive power than those with less structure. This finding implies that the environmental variables pedogeomorphologists have perceived important in the conventional regression modeling may have a reduced predictive power in reality, in cases where they possess a significant amount of SAC. This research demonstrates that accounting for spatial structure not only helps to avoid the violation of statistical assumptions, but also allows a better understanding of dynamic soil hydrological processes occurring at different spatial scales.     

Spatial precipitation and evapotranspiration in the typical steppe of Inner Mongolia,China – A model based approach using MODIS data

We present a simple method to derive spatial precipitation (P) and evapotranspiration (ET) for the typical steppe of the Xilin river catchment at 1 km and 8-day resolution during the main vegetation period (23 April to 28 August) of 2006. The hydrological model BROOK90 was parameterised from eddy covariance measurements. The daily model input data, precipitation, minimum (Ta_min) and maximum air temperature (Ta_max), were derived by manipulating MODIS leaf area index (LAI) and surface temperature data. P was estimated based on a linear regression of P measured at several sites against the mean gain of the MODIS LAI of surrounding 3 × 3 pixels areas (R² = 0.76). Ta_min and Ta_max were derived using a relationship between measured Ta_min and Ta_max and MODIS surface temperatures (R² = 0.92 and R² = 0.88, respectively). The mean precipitation was 145 mm; it varied between 52 mm in the north-western region and 239 mm in the eastern region. In spring, the modelled ET was low (<0.8 mm d⁻¹); evaporation dominated over transpiration and spatial differences were small. At the end of June, the mean ET reached its maximum (2 mm d⁻¹) and spatial differences were pronounced. From July on, transpiration dominated over declining evaporation, and spatial differences decreased in August.     

Simulation of soil erosion and deposition in a changing land use: A modelling approach to implement the support practice factor

Using the USPED (Unit Stream Power Erosion Deposition) model, three land use scenarios were analysed for an Italian small catchment (15 km²) of high landscape value. The upper Orme stream catchment, located in the Chianti area, 30 km south of Florence, has a long historical agriculture record. Information on land use and soil conservation practices date back to 1821, hence offering an opportunity to model impacts of land use change on erosion and deposition. For this study, a procedure that takes into account soil conservation practices and potential sediment storage is proposed. The approach was to calculate and model the flow accumulation considering rural and logging roads, location of urban areas, drainage ditches, streams, gullies and permanent sediment sinks. This calculation attempts to assess the spatial variability, especially the impact of support practices (P factor). Weather data from 1980–2003 were taken into account to calculate the R factor. However, to consider the intense pluviometric conditions in terms of the erosivity factor, the 0.75th quantile was used, while the lowest erosivity was modelled using the 0.25th quantile. Results of the USPED model simulation show that in 1821 the mean annual net erosion for the watershed was 2.8 Mg ha^− 1 y^− 1; in 1954 it was 4.2 Mg ha^− 1 y^− 1; and in 2004 it was 5.3 Mg ha^− 1 y^− 1. Conservation practices can reduce erosion processes by ≥ 20 Mg ha^− 1 y^− 1 when the 1821 practices are introduced in the present management. On the other hand, if the support practices are not considered in the model, soil erosion risk is overestimated. Field observation for the present-day simulation confirmed that erosion and associated sediment deposition predicted by the model depend, as expected, on geomorphology and land use. The model shows limitations that are mainly due to the input data. A high resolution DEM is essential for the delineation of reliable topographic potential to predict erosion and deposition especially in vineyards.     

Knowledge transfer and adaptation for land-use simulation with a logistic cellular automaton

Few studies have been conducted into the use of knowledge transfer for tackling geo-simulation problems. Cellular automata (CA) have proven to be an effective and convenient means of simulating urban dynamics and land-use changes. Gathering the knowledge required to build the CA may be difficult when these models are applied to large areas or long periods. In this paper, we will explore the possibility that the knowledge from previously collected data can be transferred spatially (a different region) and/or temporally (a different period) for implementing urban CA. The domain adaptation of CA is demonstrated by integrating logistic-CA with a knowledge-transfer technique, the TrAdaBoost algorithm. A modification has been made to the TrAdaBoost algorithm by incorporating a dynamicweight-trimming technique. This proposed model, CA_trans, is tested by choosing different periods and study areas in the Pearl River Delta. The ‘Figure of Merit’ measurements in the experiments indicate that CA_trans can yield better simulation results. The variance of traditional logistic-CA is about 2–5 times the variance of CA_trans until the number of new data reaches 30. The experiments have demonstrated that the proposed method can alleviate the sparse data problem using knowledge transfer.     

Peak Oil: Testing Hubbert’s Curve via Theoretical Modeling

A theoretical model of conventional oil production has been developed. The model does not assume Hubbert’s bell curve, an asymmetric bell curve, or a reserve-to-production ratio method is correct, and does not use oil production data as an input. The theoretical model is in close agreement with actual production data until the 1979 oil crisis, with an R ² value of greater than 0.98. Whilst the theoretical model indicates that an ideal production curve is slightly asymmetric, which differs from Hubbert’s curve, the ideal model compares well with the Hubbert model, with R ² values in excess of 0.95. Amending the theoretical model to take into account the 1979 oil crisis, and assuming the ultimately recoverable resources are in the range of 2–3 trillion barrels, the amended model predicts conventional oil production to peak between 2010 and 2025. The amended model, for the case when the ultimately recoverable resources is 2.2 trillion barrels, indicates that oil production peaks in 2013.     

1985-2005年环渤海地区土地利用变化时空格局

Based on TM image data and other survey materials, this paper analyzed the spatiotemporal patterns of land use change in the Bohai Rim during 1985–2005. The findings of this study are summarized as follows: (1) Land use pattern changed dramatically during 1985–2005. Industrial and residential land in urban and rural areas increased by 643,946 hm², of which urban construction land had the largest and fastest increase of 294,953 hm² at an annual rate of 3.72%. (2) The outward migration of rural population did not prevent the expansion of residential land in rural areas by 184,869 hm². This increase reveals that construction of rural residences makes seriously wasteful and inefficient use of land. (3) Arable land, woodland and grassland decreased at a rate of −0.02%, −0.12% and −1.32% annually, while unused land shrank by 157,444 hm² at an annual rate of −1.69%. (4) The change of land use types showed marked fluctuations over the two stages (1985–1995 and 1995–2005). In particular, arable land, woodland and unused land experienced an inversed trend of change. (5) There was a significant interaction between arable land and woodland. Industrial construction land in urban and rural areas showed a net trend of increase during the earlier period, but only adjustment to its internal structure during the second period. The loss of arable land to the construction of factories, mines and residences took place mainly in the fringe areas of large and medium-sized cities, along the routes of major roads, as well as in the economically developed coastal areas in the east. Such changes are closely related to the spatial differentiation of the level of urbanization and industrialization in the region.     

High spatial resolution hyperspectral mapping of in-stream habitats, depths, and woody debris in mountain streams

This article evaluates the potential of 1-m resolution, 128-band hyperspectral imagery for mapping in-stream habitats, depths, and woody debris in third- to fifth-order streams in the northern Yellowstone region. Maximum likelihood supervised classification using principal component images provided overall classification accuracies for in-stream habitats (glides, riffles, pools, and eddy drop zones) ranging from 69% for third-order streams to 86% for fifth-order streams. This scale dependency of classification accuracy was probably driven by the greater proportion of transitional boundary areas in the smaller streams. Multiple regressions of measured depths (y) versus principal component scores (x₁, x₂,…, x_n) generated R² values ranging from 67% for high-gradient riffles to 99% for glides in a fifth-order reach. R² values were lower in third-order reaches, ranging from 28% for runs and glides to 94% for pools. The less accurate depth estimates obtained for smaller streams probably resulted from the relative increase in the number of mixed pixels, where a wide range of depths and surface turbulence occurred within a single pixel. Matched filter (MF) mapping of woody debris generated overall accuracies of 83% in the fifth-order Lamar River. Accuracy figures for the in-stream habitat and wood mapping may have been misleadingly low because the fine-resolution imagery captured fine-scale variations not mapped by field teams, which in turn generated false “misclassifications” when the image and field maps were compared.The use of high spatial resolution hyperspectral (HSRH) imagery for stream mapping is limited by the need for clear water to measure depth, by any tree cover obscuring the stream, and by the limited availability of airborne hyperspectral sensors. Nonetheless, the high accuracies achieved in northern Yellowstone streams indicate that HSRH imagery can be a powerful tool for watershed-wide mapping, monitoring, and modeling of streams.     

Assessing vegetation cover and biomass in restored erosion areas in Iceland using SPOT satellite data

Due to highly erodible volcanic soils and a harsh climate, livestock grazing in Iceland has led to serious soil erosion on about 40% of the country's surface. Over the last 100 years, various revegetation and restoration measures were taken on large areas distributed all over Iceland in an attempt to counteract this problem. The present research aimed to develop models for estimating percent vegetation cover (VC) and aboveground biomass (AGB) based on satellite data, as this would make it possible to assess and monitor the effectiveness of restoration measures over large areas at a fairly low cost. Models were developed based on 203 vegetation cover samples and 114 aboveground biomass samples distributed over five SPOT satellite datasets. All satellite datasets were atmospherically corrected, and digital numbers were converted into ground reflectance. Then a selection of vegetation indices (VIs) was calculated, followed by simple and multiple linear regression analysis of the relations between the field data and the calculated VIs.Best results were achieved using multiple linear regression models for both %VC and AGB. The model calibration and validation results showed that R² and RMSE values for most VIs do not vary very much. For percent VC, R² values range between 0.789 and 0.822, leading to RMSEs ranging between 15.89% and 16.72%. For AGB, R² values for low-biomass areas (AGB < 800 g/m²) range between 0.607 and 0.650, leading to RMSEs ranging between 126.08 g/m² and 136.38 g/m². The AGB model developed for all areas, including those with high biomass coverage (AGB > 800 g/m²), achieved R² values between 0.487 and 0.510, resulting in RMSEs ranging from 234 g/m² to 259.20 g/m². The models predicting percent VC generally overestimate observed low percent VC and slightly underestimate observed high percent VC. The estimation models for AGB behave in a similar way, but over- and underestimation are much more pronounced.These results show that it is possible to estimate percent VC with high accuracy based on various VIs derived from SPOT satellite data. AGB of restoration areas with low-biomass values of up to 800 g/m² can likewise be estimated with high accuracy based on various VIs derived from SPOT satellite data, whereas in the case of high biomass coverage, estimation accuracy decreases with increasing biomass values. Accordingly, percent VC can be estimated with high accuracy anywhere in Iceland, whereas AGB is much more difficult to estimate, particularly for areas with high-AGB variability.     

Evaluating the Temporal and Spatial Urban Expansion Patterns of Guangzhou from 1979 to 2003 by Remote Sensing and GIS Methods

China has experienced and is experiencing expeditious urban expansion in the recent decades, especially in the coastal areas and big cities. Rapid urban expansion and dramatic changes of landscape have caused great economic, environmental and social impacts consequently. It is crucial to understand urban temporal, spatial expansion patterns and their related effects. In this paper, urban expansion of Guangzhou, a rapid growing city in south‐east China, from 1979 to 2003 is studied temporally and spatially. Four time ranges including 1979–1990, 1990–1995, 1995–2000 and 2000–2003 are designed and the urban expansion area, expansion rate and the spatial expansion pattern are discussed by using remote sensing data and Geographical Information System (GIS) tool. Two transects are designed along two axes of Guangzhou expansion and the structural of urban expansion patches at different orientations are compared in order to quantitively understand the urban expansion of Guangzhou during the past 24 years. The gradient analysis integrating multi‐temporal data is performed in order to analyze and compare the spatial and temporal dynamics of urban expansion. Two indices of compactness and fractal dimensional index are used to describe the urban developing pattern in the study time durations. And the influence of different types of traffic roads to urban expansion is evaluated using the buffer analysis of GIS. The results show that: (1) temporally, urban area of Guangzhou increase 296.54 km² from 141.15 km² in 1979 to 437.70 km² in 2003 and the increasing rate is up to 210.08%; (2) spatially, Guangzhou has different urban expanding directions in different stages and the general expanding directions are towards northeast, north, southeast and north in four studied time ranges; (3) transportation lines play a very important role in urban expansion of Guangzhou, but different types of road have different impacts. National roads and highways exhibit stronger control to urban expansion than provincial roads; and (4) expansion of Guangzhou has gradually changed from a compact pattern to leapfrogging and disordering patterns.     

Fractal dimension and fractal growth of urbanized areas

Based on a box-accounting fractal dimension algorithm (BCFD) and a unique procedure of data processing, this paper computes planar fractal dimensions of 20 large US cities along with their surrounding urbanized areas. The results show that the value range of planar urban fractal dimension (D) is 1< D <2, with D for the largest city, New York City, and the smallest city, Omaha being 1.7014 and 1.2778 respectively. The estimated urban fractal dimensions are then regressed to the total urbanized areas, Log (C), and total urban population, Log (POP), with log-linear functions. In general, the linear functions can produce good-fits for Log (C) vs. D and Log (POP) vs. in terms of R₂ values. The observation that cities may have virtually the same D or Log(C) value but quite disparate population sizes indicates that D itself says little about the specific orientation and configuration of an urban form and is not a good measure of urban population density. This paper also explores fractal dimension and fractal growth of Baltimore, MD for the 200-year span from 1792–1992. The results show that Baltimore's D also satisfies the inequality 1< D <2, with D =1.0157 in 1822 and D =1.7221 in 1992. D =0.6641 for Baltimore in 1792 is an exception due mainly to its relatively small urban image with respect to pixel size. While D always increases with Log (C) over the years, it is not always positively correlated to urban population, Log(POP).     

Validation of WRF model on simulating forcing data for Heihe River Basin

The research of coupling WRF (Weather Research and Forecasting Model) with a land surface model is enhanced to explore the interaction of the atmosphere and land surface; however, regional applicability of WRF model is questioned. In order to do the validation of WRF model on simulating forcing data for the Heihe River Basin, daily meteorological observation data from 15 stations of CMA (China Meteorological Administration) and hourly meteorological observation data from seven sites of WATER (Watershed Airborne Telemetry Experimental Research) are used to compare with WRF simulations, with a time range of a whole year for 2008. Results show that the average MBE (Mean Bias Error) of daily 2-m surface temperature, surface pressure, 2-m relative humidity and 10-m wind speed were ?0.19 °C, ?4.49 hPa, 4.08% and 0.92 m/s, the average RMSE (Root Mean Square Error) of them were 2.11 °C, 5.37 hPa, 9.55% and 1.73 m/s, and the average R (correlation coefficient) of them were 0.99, 0.98, 0.80 and 0.55, respectively. The average MBE of hourly 2-m surface temperature, surface pressure, 2-m relative humidity, 10-m wind speed, downward shortwave radiation and downward longwave were ?0.16 °C, ?6.62 hPa, ?5.14%, 0.26 m/s, 33.0 W/m² and ?6.44 W/m², the average RMSE of them were 2.62 °C, 17.10 hPa, 20.71%, 2.46 m/s, 152.9 W/m² and 53.5 W/m², and the average R of them were 0.96, 0.97, 0.70, 0.26, 0.91 and 0.60, respectively. Thus, the following conclusions were obtained: (1) regardless of daily or hourly validation, WRF model simulations of 2-m surface temperature, surface pressure and relative humidity are more reliable, especially for 2-m surface air temperature and surface pressure, the values of MBE were small and R were more than 0.96; (2) the WRF simulating downward shortwave radiation was relatively good, the average R between WRF simulation and hourly observation data was above 0.9, and the average R of downward longwave radiation was 0.6; (3) both wind speed and rainfall simulated from WRF model did not agree well with observation data.