Spatial

问题 I'm working on a 2D game that has a huge amount of dynamic entities. For fun's sake, let's call them soldiers, and let's say there are 50000 of them (which I just randomly thought up, it might be much more or much less :)). All these soldiers are moving every frame according to rules - think boids / flocking / steering behaviour. For each soldier, to update it's movement I need the X soldiers that are closest to the one I'm processing. What would be the best spatial hierarchy to store them to

问题 I was reading this question, I wanted to ask more about the code that he showed i.e for(i = 0; i < 20; i++) for(j = 0; j < 10; j++) a[i] = a[i]*j; The questions are, I understand temporal locality, I think that references to i and j should be temporal locality. Am I right? I also understand spatial locality, as the question I linked answers that references to a[i] should be spatial locality. Am I right? The person said, "The inner loop will call same memory address when accessing a[i] ten

问题 Suppose I have yearly precipitation data for 100 stations from 1951 to 1980. In some papers, I find people apply PCA to the time series and then plot the spatial loadings map (with values from -1 to 1), and also plot the time series of the PCs. For example, figure 6 in https://publicaciones.unirioja.es/ojs/index.php/cig/article/view/2931/2696 is the spatial distribution of the PCs. I am using function prcomp in R and I wonder how I can do the same thing. In other words, how can I extract the

问题 I have a locations table that has many rows of locations and data. There are 2 columns inside of that - Longitude and Latitude. I want to find records from that table that are found in a defined map polygon area. I have a second table that I just created based on some tutorials called polyThing which has my defined boundary... CREATE TABLE polyThing ( `ID` int auto_increment primary key, `boundary` polygon not null, `testarea` varchar(200) NOT NULL ); INSERT INTO polyThing (boundary, testarea

问题 Edit: As suggested by Edzer Pebesma in the comments, the recommended method to add a hole to a polygon is not to modify the slot but to rebuild the polygon, as illustrated in this related question. Original question Following the help of SpatialPolygons-class I tried to modify a polygon to be a hole of an other polygon but while the "hole polygon" is displayed as a border of the other polygon, its inside is colored as the rest. What am I doing wrong? Using defPunched and defHole defined here:

问题 I am trying to spatially interpolate a dataset of seawater pH by lat & long: sample<-read.csv(file="Station locations 2016.csv", header=TRUE, sep=",", strip.white=T) head(sample) Station lat long pH 1 B17 -23.49174 152.0718 8.222411 2 B23 -23.49179 152.0718 8.199310 3 B26 -23.49182 152.0717 8.140428 4 B28 -23.49183 152.0717 8.100752 5 B30 -23.49185 152.0717 8.068141 6 B31 -23.49187 152.0717 8.048852 I have created a grid based on the existing ranges in lat/long data and want to interpolate

问题 Options $lat = '25.7742658'; $lng = '-80.1936589'; $miles = 30; Query SELECT *, ( 3959 * acos( cos( radians($lat) ) * cos( radians( lat ) ) * cos( radians( lng ) - radians($lng) ) + sin( radians($lat) ) * sin( radians( lat ) ) ) ) AS distance FROM locations HAVING distance < $miles ORDER BY distance LIMIT 0, 20 I have a database table with 4 columns: unique id city name latitude (lat) longitude (lng) I'm using the query on top to return locations that are within a specified number of miles

问题 Hi have some question regarding spatial and temporal locality. I have read in the course theory that spatial locality If one item is referenced, the likelihood of other address close by will be referenced soon temporal locality One item that is referenced at one point in time it tend to be referenced soon again. Ok, but how do I see that in the code? I think I understood the concept for temporal locality but I don't understand spatial locality yet. For instance in this loop for(i = 0; i < 20;

问题 I'm having some fun trying to pick a decent SQL Server 2008 spatial index setup for a data set I am dealing with. The dataset is polygons, representing contours over the whole globe. There are 106,000 rows in the table, the polygons are stored in a geometry field. The issue I have is that many of the polygons cover a large portion of the globe. This seems to make it very hard to get a spatial index that will eliminate many rows in the primary filter. For example, look at the following query:

问题 I want to calculate the distance between approx. 100,000 different ZIP codes. I know about the mapdist function in the ggmap package mapdist works perfectly: library(ggmap) mapdist('Washington', 'New York', mode = 'driving') # from to m km miles seconds minutes hours # 1 Washington New York 366284 366.284 227.6089 13997 233.2833 3.888056 mapdist('20001', '10001', mode = 'driving') # from to m km miles seconds minutes hours # 1 20001 10001 363119 363.119 225.6421 13713 228.55 3.809167 However,