New approach uses satellite imagery to estimate economic growth.
problem Lack of reliable economic data in developing countries.
method Dynamic network and representation learning.
result Accurately predicts spatial gross economic expenditures.
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48 results for “city growth”
A theoretical approach for Pareto-Zipf lawphysics.soc-ph
We suggest an analytical approach for Pareto-Zipf law, where we assume random multiplicative noise and fragmentation processes for the growth of the number of citizens of each city and the number of the cities, respectively.
A Generalized Population Dynamics Model of a City and an Algorithm for Engineering Regime Shiftsphysics.soc-ph
Measures of wealth and production have been found to scale superlinearly with the population of a city. Therefore, it makes economic sense for humans to congregate together in dense settlements. A recent model of population dynamics showed that population growth can become superexponential due to the superlinear scalin…
The latest global financial tsunami and its follow-up global economic recession has uncovered the crucial impact of housing markets on financial and economic systems. The Chinese stock market experienced a markedly fall during the global financial tsunami and China's economy has also slowed down by about 2\%-3\% when m…
Understanding cities is central to addressing major global challenges from climate and health to economic resilience. Although increasingly perceived as fundamental socio-economic units, the detailed fabric of urban economic activities is only now accessible to comprehensive analyses with the availability of large data…
A common trajectory recapitulated by urban economiesphysics.soc-ph
Urban economies follow universal scaling laws over time.
problem Identify a common economic pathway for cities.
method Mathematical framework integrating cross-sectional data into longitudinal evolution.
result Urban economies recapitulate observed cross-sectional regularity over time.
Mining Urban Performance: Scale-Independent Classification of Cities Based on Individual Economic Transactionsphysics.soc-ph
Intensive development of urban systems creates a number of challenges for urban planners and policy makers in order to maintain sustainable growth. Running efficient urban policies requires meaningful urban metrics, which could quantify important urban characteristics including various aspects of an actual human behavi…
Study optimizes climate adaptation strategies for NYC.
problem Catastrophic damages from extreme weather in NYC.
method Real options analysis and extreme value theory.
result Optimal adaptation pathways identified for NYC.
We study a resource utilization scenario characterized by intrinsic fitness. To describe the growth and organization of different cities, we consider a model for resource utilization where many restaurants compete, as in a game, to attract customers using an iterative learning process. Results for the case of restauran…
A Generalized Preferential Attachment Model for Business Firms Growth Rates: II. Mathematical Treatmentphysics.soc-ph
We present a preferential attachment growth model to obtain the distribution of number of units in the classes which may represent business firms or other socio-economic entities. We found that is described in its central part by a power law with an exponent which depends on the probabil…
Deep learning tool classifies urban delivery vehicles.
problem Counting and categorizing delivery vehicles in cities.
method Developed annotated database and retrained CNNs.
result Accurate classification of 90%+ for 3 vehicle classes.
City2City translates place representations across cities using language translation techniques.
problem Lack of inter-city perspective in place representations from mobility data.
method Applied unsupervised machine language translation methods to translate place representations across different cities.
result Successfully translated place representations from one city to another, validated using landuse data.
Scaling of Urban Income Inequality in the United Statesphysics.soc-ph
Study shows income inequality increases with city size, affecting only the wealthiest deciles.
problem Understanding income inequality in urban areas.
method Urban scaling analysis of total income scaling in population percentiles.
result Income in the poorest decile does not increase with city size, while the wealthiest deciles show superlinear scaling.
Meta-learning approach for spatial-temporal prediction across cities.
problem Spatial-temporal prediction for cities with limited data.
method Meta-learning paradigm with spatial-temporal network and pattern-based memory.
result Meta-learning model improves prediction accuracy over multiple tasks.
Research uses Twitter data to analyze public perception of city logistics.
problem Understanding public views on city logistics from multiple stakeholders.
method Collecting Twitter content, applying unsupervised learning and NLP.
result Built an Interest Map and determined sentiment of city logistics entries.
I propose and briefly define the concept of Urban Isobenefit Lines by using functions as easy as efficient, whose results can offer a rich tool to use into spatial equilibrium analysis involving cities. They are line joining urban points with equal level of positional advantage from city amenities. The results which on…
Macroeconomic theories of growth and wealth distribution have an outsized influence on national and international social and economic policies. Yet, due to a relative lack of reliable, system wide data, many such theories remain, at best, unvalidated and, at worst, misleading. In this paper, we introduce a novel econom…
The scaling of income inequality in citiesphysics.soc-ph
Developing a scientific understanding of cities in a fast urbanizing world is essential for planning sustainable urban systems. Recently, it was shown that income and wealth creation follow increasing returns, scaling superlinearly with city size. We study scaling of per capita incomes for separate census defined incom…
Study uncovers uncertainty in traffic prediction models across cities.
problem Lack of interpretability in deep learning models for traffic prediction.
method Investigated uncertainty quantification methods for image-based traffic prediction.
result Meaningful uncertainty estimates can be recovered for traffic prediction.
The relationship between minimal algebraic Kac-Moody groups and twin buildings is well known as is the relationship between formal completions in one direction and affine buildings. Nevertheless, as the completion of a Kac-Moody group in one direction destroys the opposite BN-pair, there exists no longer a twin buildin…
This study uses computational methods to analyze Italo Calvino's 'Invisible Cities', improving on previous literary analysis.
problem Improving literary analysis through computational methods for a specific novel.
method Leveraging pretrained contextualized representations and unsupervised clustering methods to analyze the thematic groups of cities in Calvino's novel.
result The computational approach provides insights into the trustworthiness of Calvino's thematic categorization of cities.
The fast demographic growth, together with the concentration of the population in cities and the increasing amount of daily waste, are factors that push to the limit the ability of waste assimilation by Nature. Therefore, we need technological means to make an optimal management of the waste collection process, which r…
ALCNN predicts bike demand patterns in new cities using multi-source geographic data.
problem Inferring fine-grained bike demands in new cities with limited data.
method Extract features from POI, road networks, and nighttime light; use coPCA for adaptation; apply DWT for daily patterns; use attention-based local CNN (ALCNN).
result ALCNN outperforms other methods in predicting bike demand patterns.
The network structure of city-firm relationsphysics.soc-ph
How are economic activities linked to geographic locations? To answer this question, we use a data-driven approach that builds on the information about location, ownership and economic activities of the world's 3,000 largest firms and their almost one million subsidiaries. From this information we generate a bipartite …
City-GAN learns city architecture styles using a custom GAN architecture.
problem Learning architectural styles of cities using standard GAN and CGAN architectures.
method Proposes a custom GAN architecture to improve learning of city architectural styles.
result Demonstrates superior performance of custom GAN architecture for city architectural style learning.
Study forecasts sub-city real estate prices weekly using radar and news sentiment.
problem Limited availability of reliable real estate price indicators at neighborhood and long horizons.
method Combining satellite radar signals and news sentiment to forecast sub-city real estate prices.
result The multimodal model reduces mean absolute error by 35% at long horizons (26-34 weeks).
D-City is a diverse traffic dataset for AI development.
problem Lack of comprehensive, detailed datasets for AI in driving.
method Collects over 10,000 dashcam videos with detailed annotations.
result Advances AI in driving perception and related areas.
Study on supply chain networks using wire transfers in Brazil.
problem Understanding economic integration and specialization in Brazilian cities.
method Constructed a directed and weighted network of wire transfers between cities, analyzed centrality measures, and used econometric analysis.
result Disassortative mixing pattern in trade network, stronger after recession, and impact of court efficiency on economic transactions.
Deep neural network predicts traffic flow on city maps.
problem Short-term traffic flow prediction on high-resolution city maps.
method UNet-based deep convolutional neural network with densely connected layers.
result Best performance on the Traffic4cast challenge 2019.
Classification of cryptocurrency coins and tokens by the dynamics of their market capitalisationsphysics.soc-ph
We empirically verify that the market capitalisations of coins and tokens in the cryptocurrency universe follow power-law distributions with significantly different values, with the tail exponent falling between 0.5 and 0.7 for coins, and between 1.0 and 1.3 for tokens. We provide a rationale for this, based on a simpl…
SHARE predicts city-wide parking availability using a hierarchical graph neural network.
problem Predicting city-wide parking availability is challenging due to spatial and temporal autocorrelation.
method SHARE uses a hierarchical graph convolution structure with contextual and soft clustering blocks, a recurrent neural network, and a parking availability approximation module.
result SHARE outperforms state-of-the-art baselines in predicting city-wide parking availability.
Study shows 'Belt and Road' node cities boost digital finance in China.
problem Impact of 'Belt and Road' node cities on digital inclusive finance.
method Descriptive analysis, literature review, theoretical model, differential differential method.
result Establishment of 'Belt and Road' node cities promotes digital inclusive finance in China, with Internet development as a mediating variable.
Project analyzes traffic videos to improve Jakarta's safety.
problem Improving traffic safety in Jakarta.
method Developed a pipeline to analyze traffic videos, turning them into usable databases.
result Better understanding of traffic challenges and safety risks.
For spherical Tits buildings of the classical types there are well-known explicit descriptions as flag complexes. Similarly for affine buildings of the classical types there are explicit constructions in terms of lattices. In this article we generalize the flag complex description to twin cities, a generalization of tw…
Aiming at quantifying and evaluating the regional commercial environment along with the level of economic development among cities in mainland China, the concept of China City Commercial Environment Credit Index(CEI) was first introduced and established in 2010. In this manuscript, a historical review and detailed intr…
Identifying latent activity behaviors and lifestyles using mobility data to describe urban dynamicsphysics.soc-ph
Study of urban lifestyles from mobility data of 1.2M people in 11 U.S. cities.
problem Lack of interpretability in digital mobility data for understanding urban lifestyles.
method Privacy-enhanced dataset of mobility visitation patterns, latent activity behavior decomposition.
result Detected 12 latent activity behaviors that describe urban lifestyles, not single lifestyles.
Integrated analysis of the urban water-electricity demand nexus in the Midwestern United Statescs.LG
Model predicts climate-sensitive water and electricity use in Midwestern cities.
problem Ensuring conservation measures in growing cities under climate change.
method Statistical learning theory-based modeling framework for predicting climate-sensitive water-electricity demand nexus.
result Water use is slightly more sensitive to climate than electricity use.
Identifying current and future informal regions within cities remains a crucial issue for policymakers and governments in developing countries. The delineation process of identifying such regions in cities requires a lot of resources. While there are various studies that identify informal settlements based on satellite…
Greedy algorithms and Zipf lawscond-mat.stat-mech
We consider a simple model of firm/city/etc. growth based on a multi-item criterion: whenever entity B fares better that entity A on a subset of items out of , the agent originally in A moves to B. We solve the model analytically in the cases and . The resulting stationary distribution of siz…
Smart city surveillance benefits from sound event recognition.
problem Improving monitoring capabilities in smart cities.
method Exploration of several classifiers on the SESA dataset.
result SGD achieved 72.13% accuracy in sound event recognition.
New model predicts citywide air pollution using deep learning.
problem Predicting air quality for entire cities is challenging due to spatiotemporal factors.
method Convolutional Long Short-Term Memory (ConvLSTM) model for spatiotemporal data.
result ConvLSTM model outperforms other methods in predicting citywide air pollution.
Estimating the drivers of urban economic complexity and their connection to economic performancephysics.soc-ph
Model estimates urban capabilities driving economic performance.
problem Estimating the drivers of urban economic complexity and their connection to performance.
method Derived a model to infer capabilities from employment data, statistically superior to alternatives.
result The derived model explains known urban scaling and economic complexity results and correlates with economic performance.
Probabilistic flows of inhabitants in urban areas and self-organization in housing marketsphysics.soc-ph
We propose a simple probabilistic model to explain the spatial structure of the rent distribution of housing market in city of Sapporo. Here we modify the mathematical model proposed by Gauvin et. al. Especially, we consider the competition between two distances, namely, the distance between house and center, and the d…
Neural networks predict traffic flow in smart cities.
problem Forecasting stochastic and nonlinear traffic flow.
method Various recurrent neural networks trained on intersection data.
result Vector output model with gated recurrent units performed best.
Shift in house price estimates during COVID-19 reveals effect of crisis on collective speculationq-fin.GN
Study shows houses appreciated more during pandemic due to speculation, not just price uncertainty.
problem Impact of COVID-19 on house prices and speculation.
method Quasi-experimental design, unit-level matching, multivariate difference-in-difference regression.
result Properties listed for sale appreciated an additional 1% per month after pandemic onset, with an excess annual growth of 12.7 percentage points.
The study clusters neighborhoods based on childhood vulnerability data and program retention rates.
problem Improving childhood outcomes and decision-making in early childhood development.
method Data-driven approaches combining public and private sources to visualize children's experiences and analyze program retention rates.
result Neighborhoods can be clustered based on childhood vulnerability, and certain programs have higher retention rates.
Two neural network models analyze bus system efficiency and demand.
problem Identify service gaps and quantify demand in public transportation.
method Two neural network models considering demographic data and metrics.
result Models can generalize to other cities' bus systems.
Model infers functions for attributes using multi-aggregate datasets with knowledge transfer.
problem Modeling aggregate data with varying granularities and spatial supports.
method Multi-output Gaussian process (MoGP) with linear mixing of independent latent GPs, aggregation process, and prior distribution of mixing weights.
result Proposed model outperforms in refining coarse-grained aggregate data.