Spatial Structure Characteristics Detecting of Landform based on Improved 3D Lacunarity Model

The spatial structure characteristics of landform are the foundation of geomorphologic classification and recognition.This paper proposed a new method on quantifying spatial structure characteristics of terrain surface based on improved 3D Lacunarity model.Lacunarity curve and its numerical integration are used in this model to improve traditional classification result that different morphological types may share the close value of indexes based on global statistical analysis.Experiments at four test areas with different landform types show that improved 3D Lacunarity model can effectively distinguish different morphological types per texture analysis.Higher sensitivity in distinguishing the tiny differences of texture characteristics of terrain surface shows that the quantification method by 3D Lacu-narity model and its numerical integration presented in this paper could contribute to improving the accuracy of land-form classifications and relative studies.

The impact of resolution on the accuracy of hydrologic data derived from DEMs

Hydrologic data derived from digital elevation models (DEM) has been regarded as an effective method in the spatial analysis of geographical information systems (GIS). However, both DEM resolution and terrain complexity has impacts on the accuracy of hydrologic derivatives. In this study, a multi-resolution and multi-relief comparative approach was used as a major methodology to investigate the accuracy of hydrologic data derived from DEMs. The experiment reveals that DEM terrain representation error affects the accuracy of DEM hydrological derivatives (drainage networks and watershed etc.). Coarser DEM resolutions can usually cause worse results. However, uncertain result commonly exists in this calculation. The derivative errors can be found closely related with DEM vertical resolution and terrain roughness. DEM vertical resolution can be found closely related with the accuracy of DEM hydrological derivatives, especially in the smooth plain area. If the mean slope is less than 4 degrees, the derived hydrologic data are usually unreliable. This result may be helpful in estimating the accuracy of the hydrologic derivatives and determining the DEM resolution that is appropriate to the accuracy requirement of a particular user. By applying a threshold value to subset the cells of a higher accumulation flow, a stream network of a specific network density can be extracted. Some very important geomorphologic characteristics, e.g., shallow and deep gullies, can be separately extracted by means of adjusting the threshold value. However, such a flow accumulation- based processing method can not correctly derive those streams that pass through the working area because it is hard to accumulate enough flow direction values to express the stream channels at the stream's entrance area. Consequently, errors will definitely occur at the stream抯 entrance area. In addition, erroneous derivatives can also be found in deriving some particular rivers, e.g., perched (hanging up) rivers, anastomosing rivers and braided rivers. Therefore, more work should be done to develop and perfect the algorithms.

Impact of neighborhood features on housing resale prices in Zhuhai (China) based on an (M)GWR model

The paper aims at exploring the relationship between housing resale prices and neighborhood features in Zhuhai,as well as structure and location characteristics.Thirteen neighborhood features are collected to analyze their influence on average community-level apartment resale prices in 2018.Six neighbor-hood features,structural and location characteristics,are selected according to their statistical significance and multi-collinearity test results from an OLS model.Regression analysis is performed by OLS,GWR,and MGWR to compare their per-formance in housing price research at community level.The comparison of the three models also demonstrates that the GWR(66%)and MGWR(68%)models perform much better than OLS model(52%).MGWR is not significantly different from GWR in areas with few sample points,and the optimal bandwidth at different spatial scales is hard to be captured in a city-level study area.The regression parameter indicates that building age is the most important factor among all influen-cing factors.Proximity to schools and factories have positive and negative significant effects on housing resale prices,respectively.The spatial pattern of neighborhood features is also detected at town level.GWR and MGWR models accurately demonstrate local spatial heterogeneity of the housing resale market,which provides better results than the traditional OLS model in the goodness of fit and parameter estimates when spatial dependency is present.The results provide references for local planning departments,helping to reveal the compli-cated relationship and spatial patterns between housing price and determinants over space.

Geographic information science in the era of geospatial big data: A cyberspace perspective

The advent of information and communication technology and the Internet of Things have led our society toward a digital era.The proliferation of personal computers,smartphones,intelligent autonomous sensors,and pervasive network interactions with individuals have gradually shifted human activities from offline to online and from in person to virtual.This transformation has brought a series of challenges in a variety of fields,such as the dilemma of placelessness,some aspects of timelessness(no time relevance),and the changing relevance of distance in the field of geographic information science(GIScience).In the last two decades,“cyber thinking”in GIScience has received significant attention from different perspectives.For instance,human activities in“cyberspace”need to be reconsidered when coupled with the geographic space to observe the first law of geography.

Does DE need a C?A proposal for a DE curriculum

The vision of a digital earth(DE)is continuously evolving,and the next-generation infrastructures,platforms and applications are being implemented.In this article,we attempt to initiate a debate within the DE and with affine communities about‘why’a digital earth curriculum(DEC)is needed,‘how’it should be developed,and‘what’it could look like.It is impossible to do justice to the Herculean effort of DEC development without extensive consultations with the broader community.We propose a frame for the debate(what,why,and how of a DEC)and a rationale for and elements of a curriculum for educating the coming generations of digital natives and indicate possible realizations.We particularly argue that a DEC is not a déjàvu of classical research and training agendas of geographic information science,remote sensing,and similar fields by emphasizing its unique characteristics.