it is like getting only half the story. The spatial processes and spatial relationships evident in the data are a primary interest and one of the reasons GIS users get so excited about spatial data analysis. To avoid an overcounting type of bias in your model, however, you must identify th...
REGRESSION ANALYSIS AND GEOGRAPHIC MODELS: REPLY David,M.,Mark,... - 《Canadian Geographer》 被引量: 12发表: 1979年 Estimation models for precipitation in mountainous regions: the use of GIS and multivariate analysis Using multiple linear regression and Geographic Information System techniques, we mo...
GIS-Assisted Regression Analysis to Identify Sources of Selenium in Streams. Water Resources Bulletin, Vol. 28, No. 2, pp. 315-330.Rosenthal, R.B., Naftz, D.L. and C. L. Qualls. 1992. GIS-assisted regression analysis to identify the sources of selenium in streams, Water Resources ...
To determine the factors influencing rock-falls, data layers of slope degree, slope aspect, slope curvature, elevation, distance to road, distance to fault, lithology, and land use were analyzed by logistic regression analysis. The results are shown as rock-fall susceptibility maps. A spatial ...
Due to the rapid development of Geographic Information Systems (GIS) in recent years, spatial data analysis has received considerable attention and played an important role in social science. Although many standard statistical techniques are attractive in traditional data analysis, they cannot...
This article presents a multidisciplinary approach to landslide susceptibility mapping by means of logistic regression, artificial neural network, and geographic information system (GIS) techniques. The methodology applied in ranking slope instability developed through statistical models (conditional analysis and...
Comparison of multivariate methods for the analysis of genetic resources and adaptation in Phytolacca dodecandra using RAPD some RAPDs and altitude, temperature and rainfall, while the variation in most RAPDs was explained by combinations of the different ecogeographical variables... K Semagn,A Bjorn...
GIS based landslide susceptibility mapping of Tevankarai Ar sub-watershed, Kodaikkanal, India using binary logistic regression analysis Landslide susceptibility mapping is the first step in regional hazard management as it helps to understand the spatial distribution of the probability of s... SE Raman...
This research addresses the methodology for landslide susceptibility mapping using multiple regression analysis and GIS tools. Based on the initial hypothesis, ten factors were recognized as effectual elements on landslide, which is geology, slope, aspect, distance from roads, faults and drainage network...
In recent years, station-level ridership forecasting models have been developed based on Geographic Information Systems (GIS) and multiple regression analysis. These models estimate the number of passengers boarding at each station as a function of the station characteristics and the areas that they se...