Spatial Patterns of Office Employment in the New York Region

This study examines the characteristics of the members in the most popular Geographic Information Systems (GIS) Professional (GISP) certification program in the United States as well as the spatial patterns of the certified GISPs. The results show that the majority of GISPs (97.3%) are located in urban areas. About 75% of the GISPs are male. Among all the GISPs, 3971 GISPs (43.3%) play a managerial role, while 4983 individuals (54.5%) assume a non-administrative role. Among the GISPs with a non-administrative role, 348 GISPs (7%) fall within the GIS Developer group, 3392 GISPs (68%) belong to the GIS Analyst group, and 1243 GISPs (25%) play other roles. Additionally, in our analysis of spatial patterns, we identified two hotspots and two coldspots. The first hotspot is centered around Idaho and Wyoming, while the second hotspot includes Virginia, Washington DC, and Maryland. One coldspot is along Iowa, Missouri, Arkansas, and Louisiana in the central U.S., while the other coldspot includes states such as Connecticut, New Jersey, and New York on the east coast. The information presented in this study can help GIS educators and practitioners develop a better understanding of the current state of this certification program in the U.S and shed light on how to further improve the GISP certification program.

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Application of multivariate statistics in detecting temporal and spatial patterns of water chemistry in Lake George, New York

Ecological Modelling ◽

10.1016/0304-3800(95)00189-1 ◽

1996 ◽

Vol 91 (1-3) ◽

pp. 183-192 ◽

Cited By ~ 24

Author(s):

B. Momen ◽

L.W. Eichler ◽

C.W. Boylen ◽

J.P. Zehr

Keyword(s):

New York ◽

Water Chemistry ◽

Multivariate Statistics ◽

Spatial Patterns ◽

Temporal And Spatial Patterns ◽

Temporal And Spatial ◽

Lake George

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Spatial patterns and vegetation?site relationships of the presettlement forests in western New York, USA

Journal of Biogeography ◽

10.1111/j.1365-2699.2006.01614.x ◽

2007 ◽

Vol 34 (3) ◽

pp. 500-513 ◽

Cited By ~ 34

Author(s):

Yi-Chen Wang

Keyword(s):

New York ◽

Spatial Patterns

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How well does Predictive Soil Mapping represent soil geography? An investigation from the USA

10.5194/soil-2021-80 ◽

2021 ◽

Author(s):

David G. Rossiter ◽

Laura Poggio ◽

Dylan Beaudette ◽

Zamir Libohova

Keyword(s):

New York ◽

Soil Properties ◽

Spatial Patterns ◽

Landscape Metrics ◽

New York State ◽

Histogram Equalization ◽

Soil Mapping ◽

Partial Replacement ◽

Field Surveys ◽

The Usa

Abstract. We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by Predictive Soil Mapping (PSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales, and compare them to spatial patterns known from detailed field surveys (gSSURGO). The methods are illustrated with an example: topsoil pH for an area in central New York State. A companion report examines other areas, soil properties, and depth slices. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case we discover and discuss substan- tial discrepancies between PSM products, as well as large differences between the PSM products and legacy field surveys. These differences are in whole-map statistics, visually-identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, co-occurence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine-learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not better than the global model, and in some cases introduced artefacts from a lithology covariate. Uncertainty (5–95% confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the PSM products as a (partial) replacement for field-based soil surveys.

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Spatial Clustering of Pedestrian Activity and the Built Environment Characteristics

Nano LIFE ◽

10.1142/s1793984418400056 ◽

2018 ◽

Vol 08 (02) ◽

pp. 1840005

Author(s):

Hao Zhang ◽

Li Yin

Keyword(s):

New York ◽

Built Environment ◽

Spatial Patterns ◽

Goodness Of Fit ◽

Spatial Clustering ◽

Poisson Model ◽

Urban Revitalization ◽

Policy Makers ◽

Global And Local ◽

The Impact

Promoting pedestrian activity has attracted increasing attention as an important strategy for the improvement of public health and urban revitalization. The impact on physical activity underpinned by built environment has been studied substantially; however, few studies had focused on the geographically varying relationships between pedestrian activity and the built environment characteristics. Built upon previous work, this study looks at the spatial patterns of pedestrian counts and the built environment contributors along two major streets in Buffalo, New York using global and local spatial autocorrelation tests and geographically weighted regression. Pedestrian generators, job density and land use mix are included as independent variables in order to study the impact on them due to the characteristics of built environment. Our findings suggest that (1) there are statistically significant clusters of street intersections with high pedestrian counts along the streets selected in our study; (2) there are some optimal sizes of clusters of pedestrian generators, which attract more pedestrians; (3) geographically weighted Poisson model helps to analyze the geographically varying relationships between the built environment and pedestrian activity with a more pronounced goodness of fit. This research contributes to the understanding of the spatial patterns of pedestrian activity and the geographically varying relationship between the built environment and pedestrian counts. Hopefully this research will help to guide and focus the minds of policy makers and urban planners alike to introduce street vitality through the modifications of the built environment, so as to improve the quality of life in their neighborhoods.

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