Nearest-Neighbor Analysis of the Spatial Distribution of Houses of Neotoma micropus in Southwestern Oklahoma

1991 ◽  
Vol 36 (2) ◽  
pp. 233 ◽  
Author(s):  
Monte Thies ◽  
William Caire
Keyword(s):  
Spatial Distribution ◽  
Nearest Neighbor ◽  
Nearest Neighbor Analysis

scholarly journals SEBARAN GUA ARKEOLOGIS DI KECAMATAN PALIYAN KABUPATEN GUNUNGKIDUL DENGAN MENGGUNAKAN ANALISIS TETANGGA TERDEKAT (NEAREST NEIGHBOURHOOD ANALYSIS)

2011 ◽  
Vol 31 (2) ◽  
pp. 122-134
Author(s):  
Harry Octavianus Sofian
Keyword(s):  
Spatial Distribution ◽  
Nearest Neighbor ◽  
Karst Area ◽  
Statistic Analysis ◽  
Colonial Era ◽  
Nearest Neighbor Analysis ◽  
Neighbourhood Analysis ◽  
Dutch Colonial

Gunung Sewu karst area has attracted the attention archaeologists since the Dutch colonial era to the present. One of the karst area is located Paliyan District, Gunungkidul Regency. Based on research conducted by Harry Octavianus Sofian in year 2007, there were at least 11 caves and rockshelter as a potential residential dwelling. This paper will discuss and look for patterns of spatial distribution of caves and archaeological potential rockshelter as an ancient settlement in the District Paliyan using Nearest Neighbor Analysis (Analisis Tetangga Terdekat) manually and use Neighborhood Statistic analysis contained in the Arc View software.

Spatial distribution of mitosis, apoptosis and small blood vessels in malignant diffuse follicular-center-cell lymphomas: A nearest-neighbor analysis

1994 ◽  
Vol 59 (3) ◽  
pp. 313-318 ◽  
Author(s):  
Rainer Kraft ◽  
Paolo Barbini ◽  
Lorenzo Leoncini ◽  
Maria T. Del Vecchio ◽  
Tiziana Megha ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Blood Vessels ◽  
Nearest Neighbor ◽  
Nearest Neighbor Analysis ◽  
Center Cell

scholarly journals Quantitative Characterization of the Spatial Distribution of Corrosion Pits Based on Nearest Neighbor Analysis

CORROSION ◽  
10.5006/3551 ◽  
2020 ◽  
Vol 76 (9) ◽  
pp. 861-870 ◽  
Author(s):  
Adeyinka Abass ◽  
Kentaro Wada ◽  
Hisao Matsunaga ◽  
Heikki Remes ◽  
Tiina Vuorio
Keyword(s):  
Spatial Distribution ◽  
Carbon Steel ◽  
Nearest Neighbor ◽  
Corrosion Process ◽  
Nacl Solution ◽  
Quantitative Characterization ◽  
Nearest Neighbor Analysis ◽  
Corrosion Pits ◽  
Pit Initiation

Nearest neighbor analysis (NNA)-based procedures are proposed for the quantitative characterization of the spatial distribution of corrosion pits in metals. After the exposure of a carbon steel to a 3.5% NaCl solution mist, the results derived from observation of corrosion pit initiation and growth were used to justify the applicability of this approach. The pits initially comprised clusters that were superimposed on a randomly distributed background set. The clustered pits subsequently coalesced, evolving into a more random pit arrangement. Furthermore, it was revealed that in the early stages, the spatial pit distribution can be predicted via inspection of surface inclusions prior to the corrosion process.

scholarly journals Road Short-Term Travel Time Prediction Method Based on Flow Spatial Distribution and the Relations

2016 ◽  
Vol 2016 ◽  
pp. 1-14 ◽  
Author(s):  
Mingjun Deng ◽  
Shiru Qu
Keyword(s):  
Time Series ◽  
Spatial Distribution ◽  
Travel Time ◽  
Nonparametric Regression ◽  
Nearest Neighbor ◽  
Nearest Neighbor Search ◽  
Short Term ◽  
Combination Model ◽  
The Road ◽  
Neighbor Search

There are many short-term road travel time forecasting studies based on time series, but indeed, road travel time not only relies on the historical travel time series, but also depends on the road and its adjacent sections history flow. However, few studies have considered that. This paper is based on the correlation of flow spatial distribution and the road travel time series, applying nearest neighbor and nonparametric regression method to build a forecasting model. In aspect of spatial nearest neighbor search, three different space distances are defined. In addition, two forecasting functions are introduced: one combines the forecasting value by mean weight and the other uses the reciprocal of nearest neighbors distance as combined weight. Three different distances are applied in nearest neighbor search, which apply to the two forecasting functions. For travel time series, the nearest neighbor and nonparametric regression are applied too. Then minimizing forecast error variance is utilized as an objective to establish the combination model. The empirical results show that the combination model can improve the forecast performance obviously. Besides, the experimental results of the evaluation for the computational complexity show that the proposed method can satisfy the real-time requirement.

scholarly journals Geospatial Distribution of Public Secondary in Gombe Local Government Area, Gombe State of Nigeria

2019 ◽  
Vol 1 (1) ◽  
pp. 30-39
Author(s):  
Yila Caiaphas Makadi ◽  
Abecca Stephen Sati ◽  
Ismail Dankaka
Keyword(s):  
Spatial Distribution ◽  
Local Government ◽  
Spatial Pattern ◽  
Nearest Neighbor ◽  
Secondary Data ◽  
Local Government Area ◽  
Primary Data ◽  
Public Secondary School ◽  
Data Survey ◽  
Senior Secondary

The paper reviews research tradition of accessibility level and spatial distribution of student in public secondary school in gombe local government area, Gombe state. Primary and secondary data were used for the study. Primary data was collected using questionnaire and a hand-held GPS receiver to capture the coordinate points of schools and other relevant data. Secondary data include administrative map, population figures of both students and Teachers, Names and addresses of the secondary schools in the study area. The data were analyzed using geographic information techniques. From the data survey carried out, the result of the analysis showed the accessibility level and spatial distribution of school in Gombe were seventeen (17) public senior secondary and total number of students were nineteen thousand and eleven (19,011). The nearest neighbor analysis (NNA) for the spatial pattern of school were carried out based on each ward in study area which as ten (10) wards in each ward revealed two major spatial distributions. The spatial pattern of the Gombe LGA has Nearest Neighbour Ratio (NNR): 3.385087, Bolari East ward with NNR: 3.385087 and Shamaki wards NNR: 1.600148, which showed dispersed pattern, while Jekada Fari ward with NNR: 0.214890, Pantami ward with NNR: 0.226863, and Herwo Gana wards with NNR: 0.185239, were showed clustered pattern. The nearest neighbor index shows clustered pattern for all the wards in the local government area except Bolari East and Shamaki wards that has dispersed pattern of distribution. The implication of these two patterns means that accessibility is poor in the study area. Students travel than normal to overcome the function of distance.

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