I was trying to do a stackplot basically like this,
The code was the following,
import pandas as pd
import matplotlib.pyplot as plt
import numpy as np
f = “move_user_prop_dist.csv”
df1 = pd.read_csv(f).set_index(“dist_bin_center”)
df1 = df1.drop(df1.columns,axis=1).transpose()
x = np.array(df1.index)
y1 = df1
y2 = df1
y3 = df1
fig,ax = plt.subplots()
y = np.vstack((y1,y2,y3))
However, Error comes out:
TypeError: ufunc ‘isfinite’ not supported for the input types, and the inputs could not be safely coerced to any supported types according to the casting rule ”safe”
Found no solution online so far. Suggestions welcomed.
Authors: Yulun ZHOU & Ying LONG
Abstract: In the past decade, an explosion of data has taken place in Chinese cities due to widespread use of mobile Internet devices, Web 2.0 applications, and the development of the “Wired City.” With advances in data storage and high-performance computing, big/open urban data have opened up important avenues for urban studies, planning practice, and commercial consultancy. Urban researchers and planners are eager to make use of these abundant, sophisticated, and dynamic data to deepen their understanding on urban form and functions. However, in practice, access to such urban data is limited in China due to institutional constraints on data distribution and data holders’ hesitation to share data. And this hampers urban analytics. To draw reliable conclusions about the workings of complex urban systems, efficient and effective interoperation of multisource urban datasets is needed. Also, dealing with the heterogeneity between datasets is an equally critical challenge, especially for urban planners and government officers. They would derive value from data analytics, but have little data processing experience. To address these issues, we initiated SinoGrids (Plan Xu Xiake), a crowdsourcing platform that standardizes (or “downscales”) microscale urban data in China to facilitate its sharing and interoperation. To assess the performance evaluation of SinoGrids, we propose field-testing with actual urban data and their potential users. Digital desert, a son project of SinoGrids is also included.
Authors: Yulun ZHOU, Yimeng SONG, Jixuan CAI & Bo HUANG
Keywords: urban vibrancy, built environment, big spatial data
Abstract: Successful urban planning and design is not only about materialized order, but also about place-making and urban vibrancy. However, there have been few quantitative empirical studies of the relationship between urban vibrancy and urban physical environment (UPE). In this study, a new quantitative measurement of urban vibrancy is developed inductively based on the intensity of social and economic activities in an area. A multi-scale classification scheme for urban vibrancy is used to identify existing vibrancy centers and their multi-scale polycentric structure. Potentially vibrant areas are also explored and mapped. The results show that UPE is one of the dominant determinants of urban vibrancy. The associations between urban vibrancy and a list of selected UPE factors are investigated, and the association is found to be non-stationary for different levels of urban vibrant areas. Generally, points of interest (POI) density, road junction density, age of buildings and average housing prices are found to be the major reflectors of urban vibrancy in Shanghai. The non-stationarity is also modeled to provide appropriate suggestions for improving the UPE at each level of urban areas. The final section of the paper clarifies the causal relationships driving the statistical correlations. This study establishes a framework for urban vibrancy studies that includes evaluation, classification, and modeling. The proposed method for evaluating urban vibrancy also serves as a reminder and demonstration of how relatively abstract social concepts can be measured with big data, as long as the method is firmly based on the combination of existing empirical theories and data relevancy.